Aggregation type temperature control load adjusting capacity determining method and system considering electricity price response

Abstract

The invention provides a method and a system for determining the regulation capacity of an aggregation type temperature control load by considering electricity price response. The method comprises the following steps: calculating a user set temperature difference value under real-time electricity price by using the obtained user set temperature and real-time electricity price of each current temperature control load; calculating an indoor temperature boundary value meeting the comfort level of the user by using the temperature difference value set by the user; building a temperature control load monomer model by considering the indoor set temperature; power instruction decomposition is carried out to obtain each temperature control load power adjusting quantity, and then a temperature control load aggregation model is constructed; and constructing an objective function and a constraint condition of the aggregation type temperature control load, and solving the aggregation type temperature control load regulation capability function to obtain the aggregation type temperature control load regulation capability. Based on the method, the invention further provides a system for determining the adjusting capacity of the aggregation type temperature control load. By reasonably dispatching the temperature control load, the load can be reduced in the peak period of demand, the load can be increased in excess supply, the balance of the power system is realized, and the economical efficiency and the reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid analysis and calculation, and particularly relates to a method and system for determining the regulation capacity of aggregated temperature-controlled loads considering price response. Background Art

[0002] With the current transformation of the energy structure and the rapid development of renewable energy, the flexibility and reliability of the power system have become increasingly important. In this context, demand-side management has become an important means to improve the flexibility and reliability of the power system. As a new type of distributed controllable resource, temperature-controlled loads have significant regulation capabilities and can participate in typical interactive scenarios such as power grid peak shaving and frequency modulation, thereby enhancing the stability and economy of the power system. Temperature-controlled loads mainly refer to the management of power demand using temperature regulation equipment in buildings (such as air conditioners, heating systems, etc.). By reasonably dispatching temperature-controlled loads, the load can be reduced during peak power demand periods and increased when power supply is excessive, thus achieving the balance of the power system. This flexible load management can reduce the electricity bills of users and improve the overall economy and reliability of the power system.

[0003] However, traditional methods for calculating the regulation capacity of temperature-controlled loads usually only rely on the physical characteristics of temperature control equipment and lack a comprehensive consideration of market behavior. The physical characteristics of temperature control equipment mainly include the working principle of the temperature control equipment, the structure of the temperature control equipment, and the application field of the temperature control equipment. Moreover, traditional methods often establish temperature-controlled loads as deterministic models and fail to consider the dynamic changes in the power market, such as the impact of electricity price fluctuations on user load response. In addition, the current regulation capacity calculation method is usually a simple addition of the individual regulation capacities of temperature-controlled loads. This limitation leads to deviations in the assessment of the regulation capacity of aggregated temperature-controlled loads in practical applications, thereby affecting the dispatching decisions and operation efficiency of the power system. In addition, the aggregation process of temperature-controlled loads often involves multiple users and different types of temperature control equipment, and the mutual influence and synergy effects between these devices are not fully considered. When traditional methods evaluate the regulation capacity of temperature-controlled loads, they often assume that each load responds independently and fail to reflect the interaction between loads in reality. Therefore, the defects of traditional methods not only affect the accurate assessment of the regulation capacity of temperature-controlled loads but also limit their application potential in the actual power market.

[0004] In summary, the traditional calculation methods for the regulation capacity of temperature-controlled loads have multiple limitations, mainly reflected in the insufficient consideration of the dynamic impact of market behavior and the interaction between temperature control devices. This has led to deviations in the assessment of the regulation capacity of aggregated temperature-controlled loads in practical applications, thereby affecting the dispatching decisions and operation efficiency of the power system. However, there is still a lack of feasible methods for achieving the efficient aggregation of temperature-controlled loads while considering physical characteristics and market behavior and for calculating the regulation capacity of aggregated temperature-controlled loads. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a method and system for determining the regulation capacity of aggregated thermostatic control loads considering electricity price response. By reasonably dispatching thermostatic control loads, the load can be reduced during peak demand periods and increased when supply is in excess, thereby achieving the balance of the power system and improving economy and reliability.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for determining the regulation capacity of aggregated thermostatic control loads considering electricity price response, comprising the following steps:

[0008] Obtain the user-set temperature and real-time electricity price of each thermostatic control load currently, calculate the difference in user-set temperature of each thermostatic control load at the real-time electricity price using the user-set temperature and real-time electricity price; and calculate the indoor temperature boundary value of each thermostatic control load to meet user comfort using the difference in user-set temperature.

[0009] Construct a single thermostatic control load model considering the indoor set temperature of each thermostatic control load to meet user comfort; decompose the power command with the same state of charge of each thermostatic control load at each moment as the goal to obtain the power adjustment amount of each thermostatic control load, and then construct an aggregated thermostatic control load model.

[0010] Construct an objective function with the maximum power change amount in a preset time as the goal, construct the constraint conditions of the aggregated thermostatic control load's own conditions, and obtain the regulation capacity of the aggregated thermostatic control load by solving the optimized aggregated thermostatic control load regulation capacity function.

[0011] A system for determining the regulation capacity of aggregated thermostatic control loads considering electricity price response, comprising a preprocessing module, a model construction module, and a solution module;

[0012] The preprocessing module is used to obtain the user-set temperature and real-time electricity price of each thermostatic control load currently, calculate the difference in user-set temperature of each thermostatic control load at the real-time electricity price using the user-set temperature and real-time electricity price; and calculate the indoor temperature boundary value of each thermostatic control load to meet user comfort using the difference in user-set temperature.

[0013] The model construction module is used to construct a single thermostatic control load model considering the indoor set temperature of each thermostatic control load to meet user comfort; decompose the power command with the same state of charge of each thermostatic control load at each moment as the goal to obtain the power adjustment amount of each thermostatic control load, and then construct an aggregated thermostatic control load model.

[0014] The solving module is used to construct an objective function with the maximum power change amount at a preset time as the target, construct the constraint conditions of the aggregated temperature-controlled load's own conditions, and obtain the regulation ability of the aggregated temperature-controlled load by solving the optimized regulation ability function of the aggregated temperature-controlled load.

[0015] The effects provided in the invention content are only the effects of the embodiments, rather than all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0016] The present invention proposes a method and system for determining the regulation ability of an aggregated temperature-controlled load considering electricity price response. The method includes the following steps: obtaining the user-set temperature and real-time electricity price of each temperature-controlled load currently, calculating the difference in the user-set temperature of each temperature-controlled load at the real-time electricity price using the user-set temperature and the real-time electricity price; and calculating the indoor temperature boundary value that satisfies the user's comfort for each temperature-controlled load using the difference in the user-set temperature; constructing a single-temperature-controlled load model considering the indoor set temperature that satisfies the user's comfort for each temperature-controlled load; decomposing the power command with the same state of charge of each temperature-controlled load at each moment as the target to obtain the power regulation amount of each temperature-controlled load, and then constructing an aggregated temperature-controlled load model; constructing an objective function with the maximum power change amount at a preset time as the target, constructing the constraint conditions of the aggregated temperature-controlled load's own conditions, and obtaining the regulation ability of the aggregated temperature-controlled load by solving the optimized regulation ability function of the aggregated temperature-controlled load. Based on the method for determining the regulation ability of an aggregated temperature-controlled load considering electricity price response, a system for determining the regulation ability of an aggregated temperature-controlled load considering electricity price response is also proposed. The present invention considers the physical characteristics and market behavior of the temperature-controlled load, forms an aggregation method for the temperature-controlled load, and then incorporates the influence of the electricity price response behavior into the calculation of the regulation ability of the aggregated temperature-controlled load, overcoming problems such as the traditional method ignoring the mutual influence between temperature-controlled loads, and realizing a more accurate assessment of the regulation ability of the temperature-controlled load.

[0017] By reasonably dispatching the temperature-controlled load, the present invention can reduce the load during the peak demand period and increase the load when the supply is excessive, thereby achieving the balance of the power system and improving the economy and reliability. Description of the Drawings

[0018] Figure 1 It is a flowchart of the method for determining the regulation ability of an aggregated temperature-controlled load considering electricity price response proposed in Embodiment 1 of the present invention;

[0019] Figure 2 It is a quantization process of the influence of market behavior based on the loss aversion theory proposed in Embodiment 1 of the present invention;

[0020] Figure 3 It is a flowchart of constructing an aggregated temperature-controlled load model proposed in Embodiment 1 of the present invention;

[0021] Figure 4Flow chart for calculating the aggregated temperature-controlled load regulation capacity proposed in Embodiment 1 of the present invention;

[0022] Figure 5 User response behavior curve under the electricity price mechanism proposed in Embodiment 1 of the present invention;

[0023] Figure 6 Schematic diagram of the regulation instruction for the air-conditioning aggregate proposed in Embodiment 1 of the present invention;

[0024] Figure 7 Schematic diagram of the SOC of the air-conditioning aggregate proposed in Embodiment 1 of the present invention;

[0025] Figure 8 Schematic diagram of the indoor temperature curve proposed in Embodiment 1 of the present invention;

[0026] Figure 9 Schematic diagram of the refrigeration power curve proposed in Embodiment 1 of the present invention;

[0027] Figure 10 Schematic diagram of the normalized SOC curve proposed in Embodiment 1 of the present invention;

[0028] Figure 11 Schematic diagram of the power curve of the air-conditioning cluster proposed in Embodiment 1 of the present invention;

[0029] Figure 12 Schematic diagram of the curve of the power change amount proposed in Embodiment 1 of the present invention;

[0030] Figure 13 Schematic diagram of the upward regulation capacity curve of the air-conditioning cluster proposed in Embodiment 1 of the present invention;

[0031] Figure 14 Schematic diagram of the downward regulation capacity curve of the air-conditioning cluster proposed in Embodiment 1 of the present invention;

[0032] Figure 15 Schematic diagram of the system for determining the aggregated temperature-controlled load regulation capacity considering electricity price response proposed in Embodiment 2 of the present invention. Detailed implementation manners

[0033] To clearly illustrate the technical features of this solution, the present invention will be described in detail below through specific embodiments and in conjunction with its accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. The present invention omits the description of well-known components and processing technologies and processes to avoid unnecessarily limiting the present invention.

[0034] Embodiment 1

[0035] Embodiment 1 of the present invention proposes a method for determining the regulation ability of aggregated thermostatic loads considering electricity price response to solve the technical problem of ignoring the mutual influence between thermostatic loads in the prior art. Figure 1 It is a flowchart of the method for determining the regulation ability of aggregated thermostatic loads considering electricity price response proposed in Embodiment 1 of the present invention;

[0036] In step 1, the quantification of the influence of market behavior based on the loss aversion theory is as follows: Obtain the user-set temperature and real-time electricity price of each current thermostatic load, calculate the difference in the user-set temperature of each thermostatic load at the real-time electricity price using the user-set temperature and real-time electricity price; and calculate the indoor temperature boundary value that satisfies the user's comfort using the difference in the user-set temperature.

[0037] In step 1.1, obtain the user-set temperature T of each current thermostatic load set and the real-time electricity price p real .

[0038] In step 1.2, the quantification of the influence of market behavior based on the loss aversion theory. Figure 2 It is the quantification process of the influence of market behavior based on the loss aversion theory proposed in Embodiment 1 of the present invention; the core of the loss aversion theory is to assume that when the market behavior causes losses to a certain aspect of the user's interests, the impact on the user is greater than the impact of the user's gains or other favorable conditions in this process, and the user will therefore have a loss aversion psychology during the consumption process. Since the user's preference degree when facing gains is less than the loss aversion degree when facing the same degree of losses, therefore, the user response behavior function in the high electricity price interval presents as a convex function, and presents as a concave-convex function in the low electricity price interval. Figure 5 It is the user response behavior curve under the electricity price mechanism proposed in Embodiment 1 of the present invention;

[0039]

[0040] Among them, ΔZ set represents the temperature deviation value set by the user; ΔZ h,max represents the maximum acceptable temperature loss of the user during high electricity prices; ΔZ l,max represents the maximum acceptable temperature loss of the user during low electricity prices; p real represents the real-time electricity price; p 0 represents the initial electricity price, that is, the reference level; Δp = p real - p 0 ; Δp represents the difference between the real-time electricity price and the initial electricity price; p h,max represents the maximum acceptable electricity price of the user; p l,min represents the minimum acceptable electricity price of the user. Among them, the slope during high electricity prices represents the loss aversion sensitivity of people when facing losses, and the larger its value, the greater the degree of loss aversion of the user.

[0041] In step1.3, the process of calculating the actual user-set temperature of each temperature-controlled load using the user-set temperature difference includes:

[0042]

[0043] Among them, T max is the maximum indoor temperature setting value that satisfies the user's comfort; T min is the minimum indoor temperature setting value that satisfies the user's comfort; T set is the user-set temperature of each current temperature-controlled load.

[0044] In step 2, consider the indoor setting temperature of each temperature-controlled load that satisfies the user's comfort to construct a single temperature-controlled load model; decompose the power command with the goal of the same state of charge of each temperature-controlled load at each moment to obtain the power adjustment amount of each temperature-controlled load, and then construct a temperature-controlled load aggregation model. Figure 3 is the flowchart of constructing the temperature-controlled load aggregation model proposed in Embodiment 1 of the present invention;

[0045] In step2.1, the main function of the temperature-controlled load is to maintain the stability of the temperature in the space. Therefore, taking the space temperature as the state variable of the single temperature-controlled load model and using the first-order temperature control equation to describe the change law of the space temperature, its thermodynamic state equation is as follows:

[0046]

[0047] Among them, T j (t) is the indoor temperature of the jth temperature-controlled load at time t; T ja (t) is the outdoor temperature of the jth temperature-controlled load at time t; C j represents the equivalent heat capacity of the jth temperature-controlled load (kJ / ℃); Rj represents the equivalent thermal resistance (℃ / kW) of the j-th temperature-controlled load; P j (t) is the cooling electric power of the j-th temperature-controlled load at time t, and η j is the cooling energy efficiency coefficient of the j-th temperature-controlled load; represents T j (t)'s rate of change with respect to time t;

[0048] The regulation constraint of the temperature-controlled load is mainly determined by the indoor user's comfortable temperature. The variation range of T j (t) is:

[0049] T jmin ≤T j (t)≤T jmax ; (4)

[0050] where, T jmin is the minimum indoor set temperature value for the j-th temperature-controlled load to meet user comfort; T jmax is the maximum indoor set temperature value for the j-th temperature-controlled load to meet user comfort;

[0051] The temperature-controlled load adjusts the cooling power through start-stop control, that is:

[0052]

[0053] where, a j (t) is the start-stop state of the j-th temperature-controlled load at time t; is the rated power of the j-th temperature-controlled load;

[0054] The temperature-controlled load usually uses bang-bang control for start-stop control, and the control law is:

[0055]

[0056] where, T jset is the set temperature of the j-th temperature-controlled load; ΔT j is the temperature control hysteresis width of the j-th temperature-controlled load; a j (t - 1) is the start-stop state of the j-th temperature-controlled load at time t - 1;

[0057] When the room temperature is at the optimal temperature set value T jset and remains unchanged, the baseline power is defined as the temperature-controlled load power under steady-state conditions, specifically:

[0058]

[0059] where, P jbaseline is the baseline of the single-unit power of the j-th temperature-controlled load;

[0060] The baseline power of the temperature-controlled load cluster can be expressed as the sum of the baseline powers of individual loads, i.e.,

[0061] P baseline，agg (t) = ∑P jbaseline (t); (8)

[0062] where, P baseline，agg (t) represents the baseline power of the temperature-controlled load cluster;

[0063] The state of charge of the j-th temperature-controlled load at time t is expressed as:

[0064]

[0065] The boundary conditions of the state of charge of the j-th temperature-controlled load at time t are:

[0066] 0 ≤ soc j (t) ≤ 1; (10)

[0067] Substituting Eqs. (7) and (9) into Eq. (3), the discrete form of the physical model of the temperature-controlled load can be obtained. The discrete form of the physical model of the j-th temperature-controlled load is:

[0068] soc j (t + 1) = α j soc j (t) + β j ΔP j (t) + γ j ; (11)

[0069] soc j (t + 1) is the state of charge of the j-th temperature-controlled load at time t + 1; α j represents the first state transition parameter of the j-th temperature-controlled load; β j represents the second state transition parameter of the j-th temperature-controlled load; γ j represents the third state transition parameter of the j-th temperature-controlled load;

[0070] ΔP j (t) represents the adjustable power value of the temperature-controlled load, which needs to satisfy:

[0071] P jmin -P jbaseline (t) ≤ ΔP j (t) ≤ P jmax -P jbaseline (t); (12)

[0072] The individual model of the temperature-controlled load is expressed as:

[0073]

[0074] In step 2.2, the individual SOC of the temperature control load is normalized and aggregated to make the different types of temperature control load devices in the region comparable.

[0075] The process of decomposing the power command with the goal of making the charge state of each temperature control load monomer the same at each moment to obtain the power adjustment amount of each temperature control load and then constructing the temperature control load aggregation model includes:

[0076]

[0077] Where ΔP S It is the sum of the changes of all temperature-controlled load units;

[0078] By combining the above n equations, the power regulation expression of each temperature control load can be obtained:

[0079]

[0080] Where k represents the time step; ΔP j (k) represents the power regulation of the jth temperature control load at the kth time step;

[0081] Therefore, the differential-algebraic equations covering the temperature control load single state transfer and the overall regulation instruction decomposition algorithm are:

[0082]

[0083] ΔP 1 (k) represents the power regulation of the jth temperature control load in the first time step; soc n (k+1) represents the charge ratio of the nth temperature control load at the k+1th time step; soc n (k) represents the charge ratio of the nth temperature control load at the kth time step;

[0084] The average SOC of the temperature control load cluster is selected as the group state variable. Since the SOC of each temperature control load is the same at each moment, the average SOC of the cluster is the SOC of each monomer. Based on the simplified formula (16), the state transfer equation of the temperature control load cluster can be obtained as follows:

[0085]

[0086] soc(k+1) represents the charge ratio of the temperature control load cluster at the k+1th time step; soc(k) represents the charge ratio of the temperature control load cluster at the kth time step; ΔP Si (t) represents the power regulation of the jth temperature control load at time t;

[0087] The temperature control load aggregation model is:

[0088]

[0089] Among them, A represents the first state transition parameter after aggregation of temperature control loads; B represents the second state transition parameter after aggregation of temperature control loads; C represents the third state transition parameter after aggregation of temperature control loads; ΔP S (t) represents the power regulation amount of the temperature control load cluster; P Smin represents the minimum value of the power regulation amount of the temperature control load cluster; P Smax represents the maximum value of the power regulation amount of the temperature control load cluster; soc(t) represents the state of charge ratio of the temperature control load cluster; P Sbaseline represents the power baseline of the temperature control load cluster.

[0090] In step 3, a target function is constructed with the maximum power change amount at a preset time as the target, and constraint conditions for the conditions of the aggregated temperature control load itself are constructed. The regulation ability of the aggregated temperature control load is obtained by solving the optimized regulation ability function of the aggregated temperature control load.

[0091] The calculation of the regulation ability of the aggregated temperature control load includes two parts: constructing a target function with the maximum upward or downward power change amount over a certain period of time, constructing constraint conditions based on the conditions of the aggregated temperature control load itself, and solving the adjustable ability. Figure 4 This is the flowchart for calculating the regulation ability of the aggregated temperature control load proposed in Embodiment 1 of the present invention.

[0092] In step 3.1, the process of constructing a target function with the maximum power change amount at a preset duration as the target includes:

[0093]

[0094] Among them, represents the upward or downward power change amount of the m-th temperature control load starting from the initial time t 0 within the preset duration T; t 0 represents the initial time; T is the preset duration; ΔP is the possible power change amount of the device within the preset duration T.

[0095] The process of constructing constraint conditions for the conditions of the aggregated temperature control load itself includes constructing inequality constraints and equality constraints for the aggregated temperature control load.

[0096] In step 3.2, the inequality constraints include:

[0097] There are upper and lower limit constraints on the SOC of the aggregated temperature control load:

[0098] 0 ≤ soc(t) ≤ 1; (20)

[0099] The response power of the aggregated temperature control load is subject to upper and lower limit constraints:

[0100] PSmin ≤ΔP S (t)+P Sbaseline ≤P Smax ;(21)

[0101] In step 3.3, the equality constraints include:

[0102] There is a constraint on the minimum value of the aggregated thermostatic control load response power:

[0103]

[0104] There is a constraint on the maximum value of the aggregated thermostatic control load response power:

[0105]

[0106] There is a constraint on the power baseline of the aggregated thermostatic control load:

[0107]

[0108] There is a constraint on the state equation of the aggregated thermostatic control load:

[0109]

[0110] In step 3.4, the process of obtaining the regulation ability of the aggregated thermostatic control load by solving the optimization function of the regulation ability of the aggregated thermostatic control load includes:

[0111] The optimization function of the regulation ability of the aggregated thermostatic control load is:

[0112]

[0113] The above single-objective linear programming problem is programmed and solved by a programming tool to obtain the regulation ability of the aggregated thermostatic control load.

[0114] To fully illustrate the implementation process of the method for determining the regulation ability of the aggregated thermostatic control load considering electricity price response proposed in Embodiment 1 of the present invention. Taking the cluster control of 1000 variable-frequency air conditioners as an example, the instruction decomposition, state update process, and regulation ability calculation process when the air conditioner aggregate affects the grid instruction are demonstrated.

[0115] Update the actual user-set temperature of each air conditioner currently in the manner of step1. Some parameters of the air conditioner are shown in the following table:

[0116] Table 1: Some parameters of the air conditioner

[0117]

[0118] Use the Monte Carlo simulation method to randomly generate the corresponding number of individual air conditioners, and construct a temperature control load aggregation model based on the state equation of the temperature control load unit + the coordinated algorithm of the control instruction according to step2. Calculate the power baseline of the individual air conditioner using Equation (17), calculate the parameters of the air conditioner cluster using Equations (13) and (18), and calculate the cluster power baseline using Equation (18). During the Monte Carlo simulation process, parameters such as the thermal resistance, heat capacity, energy efficiency coefficient, and rated power of the air conditioner adopt a uniform distribution.

[0119] Finally, use the method of step3 to calculate the adjustment ability of the air conditioner. Construct an objective function for calculating the adjustment ability using Equation (19), construct constraint conditions using Equations (20)-(25), obtain an optimization model for calculating the adjustment ability of the air conditioner according to Equation (26), and use Cplex to solve the above optimization model to obtain the upper and lower adjustment abilities of the air conditioner cluster. Figure 13 Schematic diagram of the upward adjustment ability curve of the air conditioner cluster proposed in Embodiment 1 of the present invention; Figure 14 Schematic diagram of the downward adjustment ability curve of the air conditioner cluster proposed in Embodiment 1 of the present invention.

[0120] To test the response process of the air conditioner cluster, assume that the power grid issues power adjustment instructions at 4 o'clock, 8 o'clock, 10 o'clock, 14 o'clock, 18 o'clock, and 22 o'clock respectively. Figure 6 Schematic diagram of the adjustment instruction of the air conditioner aggregate proposed in Embodiment 1 of the present invention; Calculate the group SOC based on the air conditioner aggregate model under the drive of this adjustment instruction. Figure 7 Schematic diagram of the SOC of the air conditioner aggregate proposed in Embodiment 1 of the present invention. It can be seen from Figure 7 that: (1) The SOC decreases when the air conditioner cluster responds to the power grid's downward power adjustment instruction, and increases when it responds to the upward power adjustment instruction; (2) The air conditioner aggregate exhibits similar energy storage characteristics, but the self-dissipation coefficient is significantly greater than that of traditional energy storage.

[0121] Send the cluster SOC to each individual air conditioner, and further transform it into a temperature set value. Each individual air conditioner adjusts its own power according to its own set temperature to track the set temperature. Figure 8 Schematic diagram of the indoor temperature curve proposed in Embodiment 1 of the present invention; Figure 9 Schematic diagram of the refrigeration power curve proposed in Embodiment 1 of the present invention; Figure 10 Schematic diagram of the normalized SOC curve proposed in Embodiment 1 of the present invention. From Figure 8 、 Figure 9 and Figure 10It can be seen that: (1) The temperatures of each air conditioner unit are different, but the change trend is consistent with the group SOC, indicating that each air conditioner unit can track its own temperature set value; (2) The refrigeration power of the air conditioner unit will change rapidly according to the temperature set value, but there is a certain overshoot and oscillation during the adjustment process, and the transient process is related to the PI regulator parameters; (3) The SOC of each air conditioner unit is basically the same and equal to the group SOC, indicating that the SOC consistent control goal is achieved.

[0122] The relevant curves of the air conditioner cluster responding to the power regulation instruction of the power grid are as shown, Figure 11 It is a schematic diagram of the power curve of the air conditioner cluster proposed in Embodiment 1 of the present invention; Figure 12 It is a schematic diagram of the power change amount curve proposed in Embodiment 1 of the present invention; as Figure 11 and Figure 12 It can be seen that: (1) The change amount of the cluster power is the way for the air conditioner cluster to respond to the power grid regulation instruction; (2) The change amount of the cluster power can accurately track the power grid regulation instruction, but there is a certain transient oscillation process, and the maximum overshoot is about 20%, and the oscillation duration is about 15 minutes. The above simulation results show that the proposed instruction decomposition method can enable the air conditioner cluster to accurately track the adjustment instruction and maintain the SOC consistent, achieving the design goal.

[0123] The method for determining the aggregated thermostatic load regulation ability considering the electricity price response proposed in Embodiment 1 of the present invention considers the physical characteristics and market behavior of the thermostatic load, forms a thermostatic load aggregation method, and then incorporates the influence of the electricity price response behavior into the calculation of the aggregated thermostatic load regulation ability, overcoming the problems of traditional methods ignoring the mutual influence between thermostatic loads, etc., and achieving a more accurate assessment of the thermostatic load regulation ability.

[0124] The method for determining the aggregated thermostatic load regulation ability considering the electricity price response proposed in Embodiment 1 of the present invention can reduce the load during the demand peak period and increase the load when the supply is excessive by reasonably dispatching the thermostatic load, thereby achieving the balance of the power system and improving the economy and reliability.

[0125] Embodiment 2

[0126] Based on the method for determining the aggregated thermostatic load regulation ability considering the electricity price response proposed in Embodiment 1 of the present invention, Embodiment 2 of the present invention also proposes a system for determining the aggregated thermostatic load regulation ability considering the electricity price response, Figure 15 It is a schematic diagram of the system for determining the aggregated thermostatic load regulation ability considering the electricity price response proposed in Embodiment 2 of the present invention. The system includes: a preprocessing module, a model construction module, and a solution module;

[0127] The preprocessing module is used to obtain the user - set temperature and real - time electricity price of each temperature - controlled load currently, calculate the difference in the user - set temperature of each temperature - controlled load at the real - time electricity price by using the user - set temperature and the real - time electricity price; and calculate the indoor set temperature that meets the user's comfort for each temperature - controlled load by using the difference in the user - set temperature.

[0128] The model - building module is used to build a single - body model of the temperature - controlled load by considering the indoor set temperature that meets the user's comfort for each temperature - controlled load; decompose the power command with the goal of the same state of charge of each temperature - controlled load at each moment to obtain the power adjustment amount of each temperature - controlled load, and then build an aggregated model of the temperature - controlled load.

[0129] The solving module is used to build an objective function with the goal of maximizing the power change amount in a preset time, build the constraint conditions of the aggregated temperature - controlled load's own conditions, and obtain the regulation ability of the aggregated temperature - controlled load by solving the optimization function of the aggregated temperature - controlled load's regulation ability.

[0130] In the preprocessing module: The process of calculating the difference in the user - set temperature of each temperature - controlled load at the real - time electricity price by using the user - set temperature and the real - time electricity price includes: determining the user response behavior curve under the electricity price mechanism by using the user - set temperature and the real - time electricity price:

[0131]

[0132] Among them, ΔZ set represents the user - set temperature deviation value; ΔZ h,max represents the maximum allowable set - temperature loss of the user at high electricity prices; ΔZ l,max represents the maximum allowable set - temperature loss of the user at low electricity prices; p h,max represents the maximum acceptable electricity price of the user; p l,min represents the minimum acceptable electricity price of the user; p real represents the real - time electricity price; p 0 represents the initial electricity price; Δp = p real -p 0 ; Δp represents the difference between the real - time electricity price and the initial electricity price.

[0133] The process of calculating the actual user - set temperature of each temperature - controlled load by using the difference in the user - set temperature includes:

[0134]

[0135] Among them, T max is the maximum indoor temperature set value that meets the user's comfort; T min is the minimum indoor temperature set value that meets the user's comfort; T set is the user - set temperature of each current temperature - controlled load

[0136] In the process of constructing the model module, the process of constructing the single model of the temperature control load by considering the indoor temperature that meets the user's comfort for each temperature control load includes:

[0137] Taking the space temperature as the state variable of the single model of the temperature control load, using the first-order temperature control equation to describe the change law of the space temperature, and establishing the thermodynamic state equation:

[0138]

[0139] Among them, T j (t) is the indoor temperature of the j-th temperature control load at time t; T ja (t) is the outdoor temperature of the j-th temperature control load at time t; C j represents the equivalent heat capacity of the j-th temperature control load; R j represents the equivalent thermal resistance of the j-th temperature control load; P j (t) is the refrigeration electric power of the j-th temperature control load at time t, and η j is the refrigeration energy efficiency coefficient of the j-th temperature control load; represents the change rate of T j (t) with time t;

[0140] T j (t) varies within the range of:

[0141] T jmin ≤T j (t)≤T jmax ; (4)

[0142] Among them, T jmin is the minimum indoor set temperature value that meets the user's comfort for the j-th temperature control load; T jmax is the maximum indoor set temperature value that meets the user's comfort for the j-th temperature control load;

[0143]

[0144] Among them, a j (t) is the start-stop state of the j-th temperature control load at time t; is the rated power of the j-th temperature control load;

[0145]

[0146] Among them, T jset is the set temperature of the j-th temperature control load; ΔT j is the temperature control hysteresis loop width of the j-th temperature control load; a j (t - 1) is the start-stop state of the j-th temperature control load at time t - 1;

[0147] When the room temperature is at the optimal temperature set value Tjset If it remains unchanged, the baseline power is defined as the temperature control load power under steady-state conditions, specifically:

[0148]

[0149] where P jbaseline is the baseline power of the j-th temperature control load unit;

[0150] The baseline power of the temperature control load cluster is expressed as:

[0151] P baseline，agg (t) = ∑P jbaseline (t); (8)

[0152] where P baseline，agg (t) represents the baseline power of the temperature control load cluster;

[0153] The state of charge of the j-th temperature control load at time t is expressed as:

[0154]

[0155] The boundary condition of the state of charge of the j-th temperature control load at time t is:

[0156] 0 ≤ soc j (t) ≤ 1; (10)

[0157] The discrete form of the physical model of the j-th temperature control load is:

[0158] soc j (t + 1) = α j soc j (t) + β j ΔP j (t) + γ j ; (11)

[0159] soc j (t + 1) is the state of charge of the j-th temperature control load at time t + 1; α j represents the first state transition parameter of the j-th temperature control load; β j represents the second state transition parameter of the j-th temperature control load; γ j represents the third state transition parameter of the j-th temperature control load; ΔP j (t) represents the adjustable power value of the temperature control load;

[0160] P jmin - P jbaseline (t) ≤ ΔP j (t) ≤ P jmax - P jbaseline (t); (12)

[0161] The temperature-controlled load single model is expressed as:

[0162]

[0163] The process of decomposing the power command with the goal of the same state of charge of each temperature-controlled load single at each moment to obtain the power adjustment amount of each temperature-controlled load, and then constructing the temperature-controlled load aggregation model includes:

[0164]

[0165] Among them, ΔP S is the sum of the changes of all temperature-controlled load singles;

[0166] The expression of the power adjustment amount of each temperature-controlled load:

[0167]

[0168] Among them, k represents the time step; ΔP j (k) represents the power adjustment amount of the jth temperature-controlled load at the kth time step;

[0169] Therefore, the differential-algebraic equation set covering the state transition of the temperature-controlled load single and the decomposition algorithm of the overall adjustment command is:

[0170]

[0171] ΔP 1 (k) represents the power adjustment amount of the jth temperature-controlled load at the 1st time step; soc n (k + 1) represents the state of charge ratio of the nth temperature-controlled load at the k + 1th time step; soc n (k) represents the state of charge ratio of the nth temperature-controlled load at the kth time step;

[0172] The state transition equation of the temperature-controlled load cluster is:

[0173]

[0174] soc(k + 1) represents the state of charge ratio of the temperature-controlled load cluster at the k + 1th time step; soc(k) represents the state of charge ratio of the temperature-controlled load cluster at the kth time step; ΔP Si (t) represents the power adjustment amount of the jth temperature-controlled load at time t;

[0175] The temperature-controlled load aggregation model is:

[0176]

[0177] Among them, A represents the first state transition parameter after the aggregation of temperature control loads; B represents the second state transition parameter after the aggregation of temperature control loads; C represents the third state transition parameter after the aggregation of temperature control loads; ΔP S (t) represents the power adjustment amount of the temperature control load cluster; P Smin represents the minimum value of the power adjustment amount of the temperature control load cluster; P Smax represents the maximum value of the power adjustment amount of the temperature control load cluster; soc(t) represents the state of charge of the temperature control load cluster; P Sbaseline represents the power baseline of the temperature control load cluster.

[0178] In the solution module, the process of constructing the objective function with the maximum power change amount within a preset time period as the target includes:

[0179]

[0180] Among them, represents the increase or decrease power change amount of the m-th temperature control load starting from the initial time t 0 within the preset time period T; t 0 represents the initial time; T is the preset time period; ΔP is the possible power change amount of the device within the preset time period T.

[0181] The process of constructing the constraint conditions for the self-conditions of the aggregated temperature control load includes constructing the inequality constraints and equality constraints of the aggregated temperature control load.

[0182] The inequality constraints include:

[0183] There are upper and lower limit constraints on the SOC of the aggregated temperature control load:

[0184] 0 ≤ soc(t) ≤ 1; (20)

[0185] The response power of the aggregated temperature control load is subject to upper and lower limit constraints:

[0186] P Smin ≤ ΔP S (t) + P Sbaseline ≤ P Smax ; (21)

[0187] The equality constraints include:

[0188] There is a constraint on the minimum value of the response power of the aggregated temperature control load:

[0189]

[0190] There is a constraint on the maximum value of the response power of the aggregated temperature control load:

[0191]

[0192] There are constraints on the aggregated thermostatic load power baseline:

[0193]

[0194] There are constraints on the aggregated thermostatic load state equation:

[0195]

[0196] The process of obtaining the aggregated thermostatic load regulation capacity by solving the optimized aggregated thermostatic load regulation capacity function includes:

[0197] The optimized aggregated thermostatic load regulation capacity function is:

[0198]

[0199] The aggregated thermostatic load regulation capacity is obtained by programming and solving through a programming tool.

[0200] The system for determining the aggregated thermostatic load regulation capacity considering electricity price response proposed in Embodiment 2 of the present invention takes into account the physical characteristics and market behavior of the thermostatic load, forms a method for aggregating the thermostatic load, and then incorporates the influence of the electricity price response behavior into the calculation of the aggregated thermostatic load regulation capacity, overcoming the problems such as the traditional method ignoring the mutual influence between thermostatic loads, and realizing a more accurate assessment of the thermostatic load regulation capacity.

[0201] The system for determining the aggregated thermostatic load regulation capacity considering electricity price response proposed in Embodiment 2 of the present invention can reduce the load during the peak demand period and increase the load when the supply is excessive by reasonably dispatching the thermostatic load, thereby achieving the balance of the power system and improving the economy and reliability.

[0202] For the description of the relevant parts in the system for determining the aggregated thermostatic load regulation capacity considering electricity price response provided in Embodiment 2 of this application, reference can be made to the detailed description of the corresponding parts in the method for determining the aggregated thermostatic load regulation capacity considering electricity price response provided in Embodiment 1 of this application, which will not be elaborated here.

[0203] Although the specific implementation manners of the present invention are described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. Various modifications or deformations that can be made by those skilled in the art without creative labor on the basis of the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for determining the aggregated temperature control load regulation capacity considering electricity price response, characterized in that: The following steps are involved: Obtain the current user-set temperature and real-time electricity price of each temperature-controlled load, and use the user-set temperature and real-time electricity price to calculate the user-set temperature difference of each temperature-controlled load under the real-time electricity price; and use the user-set temperature difference to calculate the indoor temperature boundary value of each temperature-controlled load that satisfies the user's comfort; Considering the indoor temperature of each temperature-controlled load that meets the user's comfort level, a temperature-controlled load monomer model is constructed; with the goal of ensuring that the charge state of each temperature-controlled load monomer is the same at all times, power instructions are decomposed to obtain the power adjustment amount of each temperature-controlled load, and then a temperature-controlled load aggregation model is constructed; The objective function is constructed with the maximum power change within the preset time as the goal, and the constraints of the aggregated temperature control load's own conditions are constructed. The aggregated temperature control load regulation capacity is obtained by solving the function for optimizing the aggregated temperature control load regulation capacity.

2. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 1 is characterized in that: The process of calculating the user set temperature difference of each temperature control load under the real-time electricity price by using the user set temperature and the real-time electricity price includes: The user response behavior curve under the electricity price mechanism is determined by using the user set temperature and the real-time electricity price: Among them, ΔZ set Indicates the temperature deviation value set by the user; ΔZ h,max Indicates the maximum acceptable set temperature loss for users at high electricity prices; ΔZ l,max Indicates the maximum acceptable set temperature loss for users at low electricity prices; p h,max Indicates the highest electricity price acceptable to users; p l,min Indicates the lowest electricity price acceptable to users; p real represents the real-time electricity price; p0 represents the initial electricity price; Δp=p real -p0; Δp represents the difference between the real-time electricity price and the initial electricity price.

3. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 2 is characterized in that: The process of calculating the indoor temperature boundary value of each temperature control load to meet the user's comfort level using the user-set temperature difference includes: Among them, T max The maximum indoor temperature setting value to meet user comfort; T min The minimum indoor temperature setting value to meet user comfort; T set Set the temperature for each user currently controlling the load.

4. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 1, characterized in that: The process of building a single temperature control load model by considering the indoor temperature of each temperature control load to meet the user's comfort level includes: Taking the space temperature as the state variable of the temperature control load monomer model, the first-order temperature control equation is used to describe the law of space temperature change, and the thermodynamic state equation is established: Among them, T j (t) is the indoor temperature of the jth temperature control load at time t; T ja (t) is the outdoor temperature of the jth temperature control load at time t; C j Represents the equivalent heat capacity of the jth temperature control load; R j represents the equivalent thermal resistance of the jth temperature control load; P j (t) is the cooling power of the jth temperature control load at time t, η j is the cooling energy efficiency coefficient of the jth temperature control load; Represents T j (t) rate of change over time t; T j The range of variation of (t) is: T jmin ≤T j (t)≤T jmax ; Among them, T jmin is the minimum indoor set temperature value that satisfies the user's comfort level for the jth temperature control load; T jmax is the maximum indoor set temperature value that satisfies the user's comfort level for the jth temperature control load; Among them, a j (t) is the start / stop state of the jth temperature control load at time t; is the rated power of the jth temperature control load; Among them, T jset is the set temperature of the jth temperature control load; ΔT j is the temperature control hysteresis loop width of the jth temperature control load; a j (t-1) is the start and stop status of the jth temperature control load at time t-1; When the room temperature is at the optimal temperature setting value T jset and remain unchanged, the baseline power is defined as the temperature control load power under steady-state conditions, specifically: Among them, P jbaseline is the power baseline of the jth temperature-controlled load cell; The baseline power of the temperature-controlled load cluster is expressed as: P baseline，agg (t)=∑P jbaseline (t); Among them, P baseline，agg (t) represents the baseline power of the temperature-controlled load cluster; The charge state of the jth temperature control load at time t is expressed as: The boundary condition of the charge state of the jth temperature control load at time t is: 0≤soc j (t)≤1; The discrete form of the physical model of the j-th temperature control load is: soc j (t+1)=α j soc j (t)+β j ΔP j (t)+γ j ; soc j (t+1) is the charge state of the jth temperature control load at time t+1; α j represents the first state transfer parameter of the jth temperature control load; β j represents the second state transfer parameter of the jth temperature control load; γ j represents the third state transition parameter of the jth temperature control load; ΔP j (t) represents the adjustable power value of the temperature control load; P jmin -P jbaseline (t)≤ΔP j (t)≤P jmax -P jbaseline (t); The temperature control load monomer model is expressed as:

5. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 4 is characterized in that: The process of decomposing the power command with the goal of making the charge state of each temperature control load monomer the same at each moment to obtain the power adjustment amount of each temperature control load and then constructing the temperature control load aggregation model includes: Where ΔP S It is the sum of the changes of all temperature-controlled load units; The power adjustment expression of each temperature control load is: Where k represents the time step; ΔP j (k) represents the power regulation of the jth temperature control load at the kth time step; Therefore, the differential-algebraic equations covering the temperature control load single state transfer and the overall regulation instruction decomposition algorithm are: ΔP1(k) represents the power regulation of the jth temperature control load in the first time step; soc n (k+1) represents the charge ratio of the nth temperature control load at the k+1th time step; soc n (k) represents the charge ratio of the nth temperature control load at the kth time step; The state transfer equation of the temperature control load cluster is: soc(k+1) represents the charge ratio of the temperature control load cluster at the k+1th time step; soc(k) represents the charge ratio of the temperature control load cluster at the kth time step; ΔP Si (t) represents the power regulation of the jth temperature control load at time t; The temperature control load aggregation model is: Among them, A represents the first state transfer parameter after the temperature control load is aggregated; B represents the second state transfer parameter after the temperature control load is aggregated; C represents the third state transfer parameter after the temperature control load is aggregated; ΔP S (t) represents the power regulation of the temperature control load cluster; P Smin Represents the minimum power regulation value of the temperature control load cluster; P Smax represents the maximum value of the power regulation of the temperature control load cluster; soc(t) represents the charge ratio of the temperature control load cluster; P Sbaseline Represents the power baseline of the temperature-controlled load cluster.

6. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 5, characterized in that: The process of constructing an objective function with the maximum power change for a preset time as the goal includes: in, Represents the power change of the mth temperature control load from the initial time t0 within the preset time T, where t0 represents the initial time; T is the preset time; ΔP is the possible power change of the device within the preset time T.

7. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 6 is characterized in that: The process of constructing the constraint conditions of the aggregated temperature control load itself includes constructing the inequality constraints and equality constraints of the aggregated temperature control load.

8. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 7, characterized in that: The inequality constraints include: The aggregated temperature control load SOC has upper and lower limit constraints: 0≤soc(t)≤1; The response power of the aggregated temperature-controlled load is subject to upper and lower limits: P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; The equality constraints include: The minimum response power of the aggregated temperature control load is constrained by: The maximum response power of the aggregated temperature control load is constrained: The power baseline of the aggregated temperature control load has constraints: The aggregate temperature control load state equation has constraints:

9. The method for determining the aggregated temperature control load regulation capability considering electricity price response according to claim 8, characterized in that: The process of obtaining the aggregated temperature control load regulation capability by solving the optimized aggregated temperature control load regulation capability function includes: The optimized aggregate temperature control load regulation capability function is: The aggregate temperature control load regulation capability is solved through programming tools.

10. An aggregated temperature control load regulation capacity determination system considering electricity price response, characterized in that: It includes preprocessing module, model building module and solution module; The preprocessing module is used to obtain the current user-set temperature and real-time electricity price of each temperature-controlled load, and calculate the user-set temperature difference of each temperature-controlled load under the real-time electricity price using the user-set temperature and real-time electricity price; and calculate the indoor temperature boundary value of each temperature-controlled load that meets the user's comfort level using the user-set temperature difference; The model building module is used to build a single temperature control load model by considering the indoor set temperature of each temperature control load to meet the user's comfort level; the power command is decomposed to obtain the power adjustment amount of each temperature control load with the goal of the same charge state of each temperature control load monomer at each moment, and then the temperature control load aggregation model is built; The solution module is used to construct an objective function with the maximum power change within a preset time as the goal, construct constraints on the aggregated temperature control load's own conditions, and obtain the aggregated temperature control load regulation capacity by solving the optimized aggregated temperature control load regulation capacity function.

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