A grid peak load regulation method for air source heat pump system based on economic optimization objective

Abstract

The present application provides a grid peak-shaving method for an air source heat pump system based on an economic optimization goal, comprising the following steps: constructing a revenue model for the air source heat pump system to participate in grid peak-shaving, wherein the revenue model includes the heating cost of the air source heat pump system, the grid-side revenue from participating in grid peak-shaving, and the additional expenditure on the user side; determining a scheduling scheme for the air source heat pump system to participate in grid peak-shaving based on the economic optimization goal of the revenue model, wherein the scheduling scheme is the power setting value of the air source heat pump system in each peak-shaving cycle; and using the scheduling scheme to control the air source heat pump system to participate in grid peak-shaving. The grid peak-shaving method provided in the present application achieves the economic optimization goal by controlling the power setting value of the air source heat pump system, and can effectively transfer the peak load of the grid and reduce the peak-shaving operation cost while ensuring the comfort of users of air source heat pump heating.

Description

Technical Field

[0001] The present application belongs to the field of new energy and power grid control technology, and specifically provides a power grid peak regulation method for an air source heat pump system based on an economic optimization target. Background Art

[0002] The air source heat pump system is a small and medium-sized heat source composed of multiple distributed air source heat pump units. It is a flexible power load with huge regulation potential and can participate in the peak demand response of the power system. In the context of the power grid introducing a time-of-use electricity price policy, when the air source heat pump system participates in the peak demand response of the power system, it should take into account the indoor temperature comfort on the user side and the economic optimization operation of the air source heat pump system.

[0003] At present, there are many system control strategies that consider the costs and benefits of air source heat pump systems participating in grid peak regulation. For example, based on the demand-side response situation, the power load data on the user side, the load forecast data of the resource owner, and the fee settlement data of the operator can be analyzed and processed. The user's load characteristics can be analyzed and classified through the maximum load range, peak-to-valley difference, and qualified rate indicators of the group of users. For users with different electricity consumption levels at different times in high-load electricity consumption areas, personalized demand-side response plans are formulated and sent to the user side. Demand response subsidies are issued to encourage users to increase or decrease electricity consumption during specific periods to achieve peak shaving and valley filling and ensure stable operation of the power grid. For example, the peak-to-valley electricity price can be used as an optimization variable, and the air source heat pump system can be controlled to participate in grid peak regulation through a multi-scenario model of balanced load and a distribution network reconstruction method of a demand-side response strategy.

[0004] At present, the research on the participation of air source heat pumps in grid peak load regulation is mostly focused on the operating performance and frost suppression of heat pumps. The focus is often on the optimal operation of the air source heat pump heating system, without considering the user comfort level. In addition, due to the continuous implementation of the time-of-use electricity price policy, there is no similar research on how to reduce its own electricity costs according to the time-of-use electricity price while maximizing the grid peak load subsidy and taking into account user comfort. At the same time, for economic optimization problems, current research mostly uses classical optimization solution algorithms such as genetic algorithms or particle swarms. This type of solution algorithm relies more on appropriate initial value settings, has a long solution time, and is prone to falling into local optimal problems. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present application provides a power grid peak load regulation method of an air source heat pump system based on an economic optimization target, the method comprising the following steps:

[0006] Constructing a revenue model for the air source heat pump system to participate in the peak load regulation of the power grid, wherein the revenue model includes the heating cost of the air source heat pump system, the grid-side revenue from participating in the peak load regulation of the power grid, and the additional expenditure on the user side;

[0007] Determine a dispatching scheme for the air source heat pump system to participate in the peak load regulation of the power grid based on the economic optimization target of the revenue model, wherein the dispatching scheme is a power setting value of the air source heat pump system in each peak load regulation cycle;

[0008] The dispatching scheme is used to control the air source heat pump system to participate in the peak load regulation of the power grid.

[0009] Furthermore, in the method, the revenue model is determined based on the following steps:

[0010] The heating cost C of the air source heat pump system participating in the peak load regulation of the power grid is determined based on the following formula: load :

[0011]

[0012] Among them, c i , Δt i are the time-of-use electricity price and duration of the i-th peak load period, P D is the uncontrollable load power, P0 is the reference working power of the air source heat pump system, Adjust the power of the air source heat pump system to the target of each peak load cycle;

[0013] The grid-side benefit C of the air source heat pump system participating in grid peak regulation is determined based on the following formula: adjust

[0014]

[0015] in, is the target power of the air source heat pump system in each peak load period and a and b are proportional coefficients;

[0016] The additional expenditure C on the user side of the air source heat pump system participating in the grid peak regulation is determined based on the following formula user :

[0017]

[0018] Among them, T0 is the indoor heating reference temperature, T is the indoor temperature, T max , T min They are the upper and lower limits of the indoor temperature change caused by the user's adjustment of the air source heat pump system power, and k1 and k2 are proportional coefficients;

[0019] The revenue model is determined based on the following formula

[0020]

[0021] Furthermore, the Satisfy the following formula:

[0022]

[0023] Among them, P G is the power of the upper power feeder.

[0024] Furthermore, the economic optimization goal is:

[0025]

[0026] Preferably, the scheduling scheme is determined by the following steps:

[0027] The economic optimization objective is converted into the minimum optimization objective as follows:

[0028]

[0029] The constraints satisfied by the minimum optimization objective are:

[0030]

[0031] Among them, T out is the ambient temperature;

[0032] The augmented Lagrangian function of the air source heat pump system participating in the peak load regulation of the power grid is constructed as shown in the following formula:

[0033]

[0034] Among them, λ is the Lagrange multiplier corresponding to the constraint condition, and ρ is the penalty function term factor corresponding to the constraint condition;

[0035] Iteratively optimize based on the following formula and T, until the optimization result meets the preset end condition:

[0036]

[0037] At the end of the iterative optimization As the power setting value of the air source heat pump system in each peak regulation cycle.

[0038] Preferably, the use of the scheduling scheme to control the air source heat pump system to participate in power grid peak load regulation specifically includes the following steps:

[0039] Determine the control rate u of the air source heat pump system in each peak load period based on the power setting value;

[0040] The circulating hot water temperature of the air source heat pump system is set based on the control rate u.

[0041] Furthermore, the control rate u of the air source heat pump system is determined by the following steps:

[0042] Establishing a composite model of circulating hot water temperature change of the air source heat pump system, the composite model includes a building heat exchange part, an influence part of the air source heat pump load rate S, and an influence part of the indoor and outdoor temperature difference;

[0043] Based on the composite model, the spatial state equation of the load rate S of the air source heat pump system is established:

[0044]

[0045] in, F=P max , y = P ashp , A ω is the volume of circulating hot water, α is the heat transfer efficiency, β is the unit conversion parameter, P max , P ashp are the maximum operating power and actual power of the air source heat pump system, cop is the energy efficiency coefficient, k in is the specific heat coefficient, T set Set temperature for circulating hot water;

[0046] The sliding surface m(t) with the load rate S of the air source heat pump system as the sliding parameter is constructed based on the following formula:

[0047]

[0048] in, is the power setting value of the air source heat pump system corresponding to the current time t in the scheduling scheme, and e(t) is the power tracking error;

[0049] The control rate u of the air source heat pump system is determined based on the following formula:

[0050]

[0051] Among them, ε and k are sliding mode control coefficients.

[0052] Furthermore, the circulating hot water temperature of the air source heat pump system is set based on the control rate u, specifically, the circulating hot water temperature setting value T of the air source heat pump system at the current time t is determined based on the following formula: set,t :

[0053]

[0054] The present application provides a grid peak-shaving method for an air source heat pump system based on an economic optimization objective. By constructing an economic dispatch model corresponding to the air source heat pump system participating in grid peak-shaving, and solving the optimal dispatching plan that achieves the economic optimization objective of the model through the ADMM algorithm, it is possible to effectively transfer the peak load of the grid and reduce the peak-shaving operating cost while ensuring the comfort of users of air source heat pump heating. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a power grid peak-shaving method of an air source heat pump system based on an economic optimization target according to an embodiment of the present application;

[0056] Figure 2 It is a schematic diagram of a revenue model of an air source heat pump system participating in power grid peak load regulation according to an embodiment of the present application;

[0057] Figure 3 It is a schematic diagram of the cross-section of the power system load (excluding the air source heat pump system load) of a certain area in 2020 according to an embodiment of the present application;

[0058] Figure 4a The comparison results of the air source heat pump system according to the embodiment of the present application participating in the power grid peak load regulation experiment under the typical daily load in January;

[0059] Figure 4b The comparison results of the air source heat pump system according to the embodiment of the present application participating in the power grid peak load regulation experiment under the typical daily load in July;

[0060] Figure 4c The comparison results of the air source heat pump system according to the embodiment of the present application participating in the peak load regulation experiment of the power grid under the average daily load. DETAILED DESCRIPTION

[0061] Hereinafter, the present application will be further described based on preferred embodiments with reference to the accompanying drawings.

[0062] An air source heat pump refers to a device that uses outdoor air as a heat source and utilizes its heat to cool and heat the indoor environment. Multiple air source heat pumps can form an air source heat pump system, which can participate in the peak load regulation of the power system as the power load of the power grid during operation. By adjusting the operating power, it can respond to the peak load regulation instructions sent by the superior dispatching part, realize peak shifting and valley filling of the power grid, and achieve the purpose of smoothing the load curve of the power grid.

[0063] Since the power load of the air source heat pump system is a flexible load and can participate in the peak-shaving demand response of the power system, when it participates in the peak-shaving of the power grid, it needs to consider both the response to the peak-shaving instruction and the impact of indoor changes caused by the adjustment of the power load on user comfort. At the same time, it is also necessary to take into account the power load, user-side costs and the benefits of participating in the peak-shaving of the power grid to achieve economically optimized operation of the air source heat pump system.

[0064] To achieve this comprehensive goal, the present application provides an air source heat pump system power grid peak load regulation method based on economic optimization objectives through embodiments. Figure 1 In some embodiments, the implementation flow chart of the peak load regulation method is as follows: Figure 1 As shown, the method comprises the following steps:

[0065] Step 100, constructing a revenue model for the air source heat pump system to participate in the peak load regulation of the power grid, wherein the revenue model includes the heating cost of the air source heat pump system, the grid-side revenue from participating in the peak load regulation of the power grid, and the additional expenditure on the user side;

[0066] Step 200, determining a dispatching scheme for the air source heat pump system to participate in the peak load regulation of the power grid based on the economic optimization target of the revenue model, wherein the dispatching scheme is a power setting value of the air source heat pump system in each peak load regulation cycle;

[0067] Step 300: Use the scheduling scheme to control the air source heat pump system to participate in power grid peak regulation.

[0068] The specific implementation of the above steps 100 to 300 will be described in detail below with reference to the accompanying drawings.

[0069] Step 100 is used to construct a profit model for the air source heat pump system to participate in the peak load regulation of the power grid. Figure 2 A schematic diagram of a revenue model of an air source heat pump participating in grid peak load regulation in an embodiment of the present application is shown.

[0070] As mentioned above, each air source heat pump included in the air source heat pump system absorbs the heat contained in the outdoor air heat source and heats the circulating hot water in the circulation pipe network, thereby realizing heating for the indoor environment. In this process, multiple air source heat pumps consume electricity in the power grid as power loads and generate electricity costs;

[0071] In addition, the air source heat pump system will also generate additional costs / benefits on the power generation side and compensatory expenditures on the user side due to its participation in the peak load regulation of the power grid. Figure 2 As shown in the figure, the revenue model of air source heat pump participating in grid peak load regulation includes the heating cost part C of the air source heat pump system load , the grid-side revenue portion C caused by its participation in grid peak regulation adjust, and the additional expenditure on the user side caused by its participation in grid peak regulation C user .

[0072] Specifically, the profit model of the air source heat pump system participating in the peak load regulation of the power grid is constructed through the following steps:

[0073] A. Determine the relationship between the peak load-shaving task, the power of the air source heat pump, and the upper and lower limits of indoor temperature change perception.

[0074] An air source heat pump is a typical controllable heat load. It uses electricity to drive a pump to compress the working fluid to perform a reverse Carnot cycle, obtain low-level thermal energy from the air, and the final thermal energy obtained is about 4 to 6 times the electrical energy consumed. The operating power of the air source heat pump system can be set according to the instructions issued by the power grid to implement peak shaving and valley filling of the power grid. In an embodiment of the present application, based on the actual operation process of the air source heat pump system, the air source heat pump heating process is simplified to construct an economic scheduling model for its participation in peak load regulation.

[0075] Specifically, the real-time operating power P(t) of the air source heat pump system can be decomposed into two parts: the first part is the reference power P0 (constantly positive) of the air source heat pump system to provide heat energy to users; the second part is the adjustment power P0 of the air source heat pump system to participate in the peak load regulation of the power grid. adjust (t), where the power decomposition relationship is shown in formula (1), and the adjustment range of the adjustable power is shown in formula (2):

[0076] P ashp (t) = P0 + P adjust (t) (1),

[0077] P ajust,min ≤P adjust ≤P ajust , max (2)

[0078] In the above formula, P ajust,min is the minimum adjustable power of the air source heat pump system, P adjust,max is the maximum adjustable power of the air source heat pump system. It should be noted that P ajust,min With P adjust,max There are positive and negative values. Generally speaking, a negative value represents a load reduction, and a positive value represents a load increase.

[0079] For the simplified model of air source heat pump system participating in power grid peak regulation, the basic condition of real-time power balance needs to be met, that is, the power generation is equal to the power consumption of the power load in real time. When the power of the upper power feeder in each peak regulation period is P G , and there is uncontrollable load power P in the system D In order to meet the peak load requirements of the power grid, the target power of the air source heat pump system in each peak load period is and target adjustment power Satisfies formula (3):

[0080]

[0081] In order to optimize the scheduling of the air source heat pump system participating in the peak load regulation, it is necessary to determine the electric heating working characteristics of the heat pump system. However, the air source heat pump works based on the reverse Carnot cycle principle and transfers the low-level heat energy in the air. The energy transfer process is complicated. Considering the requirements for the accuracy of the heat pump system participating in the peak load regulation of the power grid, in the embodiment of the present application, the heating temperature and operating power of the air source heat pump system are simplified, and a local linearization model as shown in formula (4) is established at the indoor heating reference temperature T0 and reference power P0 of the air source heat pump system user, where T out Represents the ambient temperature:

[0082]

[0083] For users who use air source heat pumps for heating, the heating temperature cannot change too much, otherwise it will exceed the user's tolerance limit T for indoor temperature changes caused by the power adjustment of the air source heat pump system. max And the lower limit T min , that is, the indoor temperature T in each peak load period should satisfy formula (5):

[0084] T min ≤T≤T max (5).

[0085] B. Establish a profit model for air source heat pump systems to participate in grid peak regulation.

[0086] In order to fully tap the potential of load regulation, the power sector often adopts the peak-valley time-of-use electricity price policy at this stage to guide users to adjust their electricity usage habits. According to the peak-valley electricity price, the air source heat pump system will adjust the power consumption, so that the daily electricity cost of the air source heat pump system changes accordingly, and its power load regulation capacity also changes accordingly. According to the time-of-use electricity price policy, when the air source heat pump system participates in the peak regulation of the power system, the load side cost C in one day load The calculation method of is shown in formula (6):

[0087]

[0088] Among them, c i , Δt i are the time-of-use electricity price and duration of the i-th peak load regulation cycle respectively.

[0089] The cost change on the electricity consumption side caused by the adjustment of electricity consumption behavior will be reflected in the cost on the power generation side at a ratio a; at the same time, the power generation department will give this part of the cost to the air source heat pump system at a ratio b to encourage it to participate in the peak load regulation of the power grid. The income C obtained by the air source heat pump system from participating in the peak load regulation of the power grid is adjust As shown in formula (7):

[0090]

[0091] Based on the local linearized electric-thermal characteristic model of the air source heat pump system shown in equation (4), due to the existence of user heating effect deviation perceived temperature and user heating maximum deviation temperature in actual operation, for the deterioration of heating effect caused by the air source heat pump system participating in the peak load regulation of the power system, the user will apply for compensation C user , this part of compensation is paid by the heat pump system, and the compensation cost is shown in formula (8):

[0092]

[0093] Among them, k1 and k2 are proportional coefficients.

[0094] Taking all the above into consideration, the profit model of air source heat pump system participating in power grid peak regulation is finally determined based on the following formula (9):

[0095]

[0096] The above revenue model is and T, for the air source heat pump load, it should find the maximum benefit point between the conflicting operating goals of the power system peak-shaving demand and the user's heating demand. The peak-shaving scheduling problem of the air source heat pump system can essentially be attributed to the model predictive control problem. The appropriate control instructions are obtained by solving the optimization objective function. In the embodiment of the present application, the above-mentioned profit model takes the economic optimization objective as the objective function to be solved, that is, P adjust and T are used as decision variables for the peak load scheduling problem to achieve the economic optimization goal of the following formula (10):

[0097]

[0098] Furthermore, in step 200, the dispatching scheme for the air source heat pump system to participate in the peak load regulation of the power grid is determined through the economic optimization target of the revenue model. In the embodiment of the present application, the dispatching scheme is the power setting value of the air source heat pump system in each peak load regulation cycle.

[0099] C. Construct ADMM method to solve the model.

[0100] After building a model that meets the economic peak load regulation requirements of the air source heat pump system, it is necessary to solve it according to the constraints. In this application, the alternating direction method of multipliers (ADMM) is used to solve it. The alternating direction method of multipliers is an algorithm suitable for solving generalized convex optimization problems. It is based on the augmented Lagrangian dual method and decomposes the variable iteration process. It has a faster convergence speed while ensuring the optimality of the solution. The method was first proposed by Gabay et al. in 1976. In 2011, Boyd et al. restated the ADMM method and proved that it can be used for optimization problems of large distributed systems. The classic applicable problem of the ADMM method is shown in formula (11), and formula (12) is the constraint condition:

[0101]

[0102] stAx+Bu=C (12),

[0103] Since ADMM is a generalization and improvement of the augmented Lagrangian dual method, it is also necessary to construct the Lagrangian function corresponding to the original optimization objective function in the solution process, as shown in formula (13), where λ is the Lagrangian multiplier corresponding to the constraint condition; ρ is the penalty function term factor corresponding to the constraint condition.

[0104]

[0105] The iteration rules of the optimized variables x, u and Lagrange multiplier λ are shown in equations (14) to (16).

[0106]

[0107] λ k+1 =λ k +ρ(Ax k+1 +Bu k+1 -C) (16).

[0108] For the problem of air source heat pumps participating in power system peak load regulation, in order to apply the ADMM solution method, firstly, the maximum optimization problem shown in formula (10) is transformed into the minimum optimization problem shown in formula (17):

[0109]

[0110] According to the local linearized electrothermal characteristic model of the air source heat pump system, the constraint condition of the minimum optimization problem in the above equation is shown in equation (18):

[0111]

[0112] Next, the augmented Lagrangian function of the air source heat pump system participating in the grid peak load regulation is constructed as shown in formula (19):

[0113]

[0114] D. Iterate and optimize to determine the scheduling plan.

[0115] The minimum optimization problem shown in (17) can be solved by iteratively optimizing and T until Converge to meet the preset end condition. Specifically, perform iterative optimization through the following formula (20):

[0116]

[0117] Finally, after iterative optimization, That is, the power setting value of the air source heat pump system in each peak regulation cycle

[0118] After determining the grid peak load regulation scheduling plan in step 200, the air source heat pump system can be controlled to execute the scheduling plan in step 300 to participate in the grid peak load regulation.

[0119] Various existing scheduling schemes for air source heat pump systems to participate in peak load regulation of power systems generally involve setting a circulating hot water temperature set value. However, this control method has a slow adjustment speed and cannot meet the demand for rapid power adjustment when the air source heat pump heating system participates in peak load regulation. For this reason, in an embodiment of the present application, first, for the power set value of each peak load regulation cycle determined in step 200, the control rate u of the air source heat pump system in each peak load regulation cycle is determined by establishing a relationship between a key variable that can quickly track the power set value and the circulating hot water temperature and its set value in the system, and then the circulating hot water temperature of the air source heat pump system is set based on the determined control rate u.

[0120] Furthermore, the control rate u of the air source heat pump system is determined by the following steps:

[0121] E. Establish a composite model of the temperature change of circulating hot water in the air source heat pump system.

[0122] Controlling the air source heat pump system based on the set peak-shaving scheme is essentially using the optimal control rate to enable the power load of the air source heat pump to respond to the task quickly and accurately. To this end, we should first combine the actual engineering application scenarios to build a compliance model that includes key variables that can quickly track the peak-shaving scheme, state variables that can reflect the system status, and control variables that can control the system status.

[0123] Specifically, in an air source heat pump system, multiple distributed air source heat pumps heat the hot water in the pipeline together, and the hot water in the pipeline circulates through the pipe network to dissipate heat to the user to achieve the purpose of heat delivery. The conventional thermodynamic model of the air source heat pump system generally only contains state variables such as water temperature and indoor air temperature. However, in the embodiment of the present application, the air source heat pump system needs to quickly adjust the power according to the peak-shaving instruction. At this time, variables such as the hot water temperature in the heating network are not suitable for achieving a rapid response to the peak-shaving task because of their slow changing trend. Therefore, it is necessary to find suitable key variables based on the power load change characteristics of the air source heat pump system, and establish the relationship between system heat and power through them.

[0124] Based on the above considerations, in the composite model of circulating hot water temperature change of the air source heat pump system established in the embodiment of the present application, the load rate S of the air source heat pump system is used as the key variable for tracking the peak load setting power of the power grid, and the circulating hot water temperature setting value T in the heating network is used as the key variable for tracking the peak load setting power of the power grid. set A composite model of circulating hot water temperature change is established to control the variables. The composite model includes the building heat exchange part, the influence part of the load rate S of the air source heat pump system, and the influence part of the indoor and outdoor temperature difference. The modeling process is as follows:

[0125] First, the qualitative temperature of the fluid in heat transfer is taken as the research object. According to the relevant definition in heat transfer, the qualitative temperature of the heat medium is the average value of the supply water temperature and the return water temperature, as shown in the following formula (21):

[0126]

[0127] Among them, T w 、T s 、T r They are respectively the qualitative temperature of circulating hot water in the heating system, the water supply temperature and the water outlet temperature.

[0128] Based on the knowledge of heat transfer, the process of heat exchange between the air source heat pump system and the building when it is working is as follows:

[0129]

[0130] Among them, T in is the indoor air temperature of the building to which the air source heat pump system belongs, T w After ignoring the difference between the supply water temperature and the outlet water temperature, the average value of the two is taken as the temperature value of the circulating hot water in the air source heat pump system using formula (21); β is the unit conversion parameter; A ω is the volume of circulating hot water; α is the heat transfer efficiency; Q w The amount of heat required to heat the air in a room.

[0131] Simplifying formula (22) we can get:

[0132]

[0133] The circulating hot water temperature model established by formula (23) is used to describe the heat exchange process between the air source heat pump system and the building.

[0134] Furthermore, based on formula (23), in order to more accurately describe the power load and heat of the air source heat pump system, it is necessary to further consider the influence of the load rate S of the air source heat pump system and the influence of the indoor and outdoor temperature difference, and a composite model describing the circulating hot water temperature can be obtained:

[0135]

[0136] Compared with formula (24), the above formula adds and They represent the influence of load rate and indoor and outdoor temperature difference in the air source heat pump system, respectively. S is the load rate of the air source heat pump system, C i is the indoor and outdoor temperature difference influence rate, P max , R are the maximum power and thermal resistance of the air source heat pump heating system respectively.

[0137] It should be noted that the operation control method of the air source heat pump system is different from that of traditional temperature control loads such as air conditioners. It cannot be simply controlled to be either on or off like controlling air conditioners. That is, when regulating the air source heat pump system, it is impossible to fully open or fully close all the heat pumps in a heat pump station. For this reason, the concept of load rate S of the air source heat pump system is introduced to represent the operating power situation: when S = 1, it means that the air source heat pump system is running at full power; when S is a value between 0 and 1, such as 0.7, it means that the air source heat pump system is running at 70% power. The expression of S is shown in formula (25):

[0138]

[0139] Where P ashp is the actual power under the current load rate; cop is the energy efficiency coefficient, so:

[0140]

[0141] According to the energy consumption model of Energy Plus, the circulating hot water temperature T w Heating to the set temperature T set The required heat Q is:

[0142]

[0143] In the above formula, k p , kin are the specific heat coefficients corresponding to the first and second terms, respectively, and are integrated from the start time of the grid peak regulation period to the current time t.

[0144] Substituting formula (27) into formula (26), we can obtain:

[0145]

[0146] Represents the part affected by the indoor and outdoor temperature difference: the lower the outdoor temperature, the larger the value of this part, indicating that more heat is required, and vice versa. i The relationship with the outdoor ambient temperature can be expressed as follows:

[0147] C i =k out ·(T in -T out ) (29),

[0148] By combining equations (24) to (28), we can obtain a composite model of the temperature change of circulating hot water in the air source heat pump system:

[0149]

[0150] F. Determine the control rate u of the air source heat pump system to execute the scheduling plan.

[0151] After obtaining the above composite model, the control rate u of the air source heat pump system in each peak-shaving period can be determined according to the power setting value of each peak-shaving period. From the establishment process of the above composite model, it can be seen that the key to power control of the air source heat pump heating system lies in the reasonable control of the key variables that can quickly track the power setting value. Therefore, the key to finding the power control rate of the air source heat pump system lies in selecting appropriate key variables and control strategies.

[0152] In order to achieve rapid control of the power of the air source heat pump heating system, in the embodiment of the present application, the control rate of the air source heat pump system is determined based on the sliding mode control. The sliding mode control (SMC) is a kind of nonlinear control. The method has strong robustness and nonlinear characteristics, and has discontinuity, that is, the control structure will continue to change. Moreover, the sliding mode control has the advantage of rapid response. Its basic idea is to divide the system state space into two regions: the sliding mode region and the sliding mode surface area. In the sliding mode region, the system will slide rapidly along the sliding mode surface, thereby achieving control of the system state; while in the sliding mode surface area, the system will be affected by a certain degree of interference or noise and other uncertain factors, but these influences will not cause the system to be out of control. By designing a sliding surface, the system can slide rapidly on the surface and remain stable, thereby achieving control of the system.

[0153] In order to apply the sliding mode control theory to the control of the air source heat pump system, it is necessary to determine the parameters that can quickly track the changes of the system on the sliding surface. Considering the circulating hot water temperature T w The entire heating system is in a slow change, and it is obviously unable to quickly respond to the peak-shaving task on the sliding surface. The system load rate S corresponds to the shutdown and startup status of each air source heat pump unit and can be flexibly adjusted. Therefore, in this application, S is used as a key variable that responds to the system peak-shaving task and can control the rapid change of the system state on the sliding surface to determine the power control rate of the air source heat pump system. Specifically, the following steps are included:

[0154] First, Equation (30) is transformed into a standard state space control model, with the load rate S of the air source heat pump system as the state variable and the water temperature setting value T of the circulating hot water as set is the control quantity, and it can be deduced that:

[0155]

[0156] Among them, the load rate S and the circulating hot water temperature T w , Indoor air temperature T set The relationship can be simplified into a linear model, as shown in formula (32):

[0157] S=K(T w -T set ) (12),

[0158] Where K is the linear coefficient.

[0159] Substituting equation (32) into equation (31), we can obtain the spatial state equation of the load rate S of the air source heat pump system shown in equation (33):

[0160]

[0161] in, F=P max , y = P ashp .

[0162] Furthermore, the power setting value of each peak load regulation period is used as input and the actual power load of the air source heat pump system to construct the power tracking error, and the error is used as the sliding surface m(t) with the load rate S of the air source heat pump system as the sliding parameter:

[0163]

[0164] in, That is, the power setting value of the air source heat pump system corresponding to the current time t in the scheduling scheme obtained in step 200, and e(t) is the power tracking error;

[0165] Then the control rate u of the air source heat pump system is determined based on the following formula (35):

[0166]

[0167] Among them, ε and k are sliding mode control coefficients.

[0168] Finally, the control rate u shown in formula (35) is used to set the circulating hot water temperature (i.e., the control variable) of the air source heat pump system. The setting method is specifically to determine the circulating hot water temperature setting value T of the air source heat pump system at the current time t based on the following formula (36): set,t :

[0169]

[0170] Example 1.

[0171] The above describes in detail the various implementation steps of the embodiments of the present application. The following further describes the effect of the power grid peak-shaving method provided by the present application in conjunction with specific embodiment 1.

[0172] In this embodiment, a simulation model of the air source heat pump system is first built in the MATLAB2021a environment, and then based on the method of this application, the ADMM solution algorithm is used to achieve the scheduling of the economic optimization target of the air source heat pump system revenue model. Specifically, the simulation model includes the uncontrollable load P D , air source heat pump load P ashp and an equivalent power supply. The coefficient values ​​in each formula are shown in Table 1. Among them, K = 50 means that the ADMM algorithm iteration target is 50 times, N = 96 means that 24 hours is discretized into 96 time periods, Δt i =15min represents each discrete time period.

[0173] Table 1 Parameters of air source heat pump peak load simulation program

[0174] parameter Value parameter Value K 50 <![CDATA[T max (℃)]]> 22 N 96 <![CDATA[P0(kW)]]> 150 <![CDATA[Δt i (h)]]> 0.25 <![CDATA[P adjust,min (kW)]]> -200 <![CDATA[T0(℃)]]> 20 <![CDATA[P adjust,max (kW)]]> 200 <![CDATA[T out (℃)]]> 5 <![CDATA[k1(￥℃·h)]]> 4 <![CDATA[T1(℃)]]> 21 <![CDATA[k2(￥℃·h)]]> 4 <![CDATA[T2(℃)]]> 19 a 0.8 <![CDATA[T min (℃)]]> 18 b 0.8

[0175] The load data set of the simulation experiment is based on the load profile of the power system in a certain area in 2020 (excluding heat pump load), and the 15th day of each month is selected as the typical load profile of each month. Figure 3 At the same time, in order to have a comprehensive understanding of the load level of the region, the load data of 12 typical days are averaged to obtain the average load curve that depicts the load level of the region throughout the year. The relevant data are shown in Figure 3 Shown by the star-shaped line.

[0176] Typical application scenarios of air source heat pump systems include heating in winter and cooling in summer. In order to test the peak-shaving capability of the heat pump system in various working scenarios, a comparative experiment of air source heat pump system participating in power system peak-shaving under typical daily load in January, typical daily load in July and average load level was designed. The peak-shaving effect is shown in Figure 2. Figures 4a to 4c shown.

[0177] Comparing the original load curve before peak shaving with the load curve after peak shaving, it can be seen that the system load has a certain increase in the valley time of 0-8:00 and 22:00-24:00, and a certain decrease in the peak time of 8:00-22:00. In addition, for both typical working conditions and average loads, the air source heat pump peak shaving method proposed in this paper has achieved significant results: (1) Under the typical daily load in January, the load profile variance decreased from 779.6 to 383.0, with a profit of 27.56 yuan; (2) Under the typical daily load in July, the load profile variance decreased from 1185.5 to 687.8, with a profit of 27.57 yuan; (3) Under the average load level, the load profile variance decreased from 937.8 to 490.6, with a profit of 27.56 yuan.

[0178] Simulation results show that the heat pump can adjust its own electricity consumption behavior to balance the peak shaving and valley filling of the power system and the needs of heating utilities, significantly reducing the system operating costs.

[0179] The above is a detailed introduction to the specific implementation methods of the present application. For those skilled in the art, several improvements and modifications may be made to the present application without departing from the principles of the present application. These improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives, characterized in that: The following steps are involved: Constructing a revenue model for the air source heat pump system to participate in the peak load regulation of the power grid, wherein the revenue model includes the heating cost of the air source heat pump system, the grid-side revenue from participating in the peak load regulation of the power grid, and the additional expenditure on the user side; Determine a dispatching scheme for the air source heat pump system to participate in the peak load regulation of the power grid based on the economic optimization target of the revenue model, wherein the dispatching scheme is a power setting value of the air source heat pump system in each peak load regulation cycle; Using the dispatching scheme to control the air source heat pump system to participate in the peak load regulation of the power grid; The revenue model is determined based on the following steps: The heating cost C of the air source heat pump system participating in the peak load regulation of the power grid is determined based on the following formula: load : Among them, c i , Δt i are the time-of-use electricity price and duration of the i-th peak load period, P D is the uncontrollable load power, P0 is the reference working power of the air source heat pump system, Adjust the power of the air source heat pump system to the target of each peak load cycle; The grid-side benefit C of the air source heat pump system participating in grid peak regulation is determined based on the following formula: adjust in, is the target power of the air source heat pump system in each peak load period and a and b are proportional coefficients; The additional expenditure C on the user side of the air source heat pump system participating in the grid peak regulation is determined based on the following formula user : Among them, T0 is the indoor heating reference temperature, T is the indoor temperature, T max 、T min They are the upper and lower limits of the indoor temperature change caused by the user's adjustment of the air source heat pump system power, and k1 and k2 are proportional coefficients; The revenue model is determined based on the following formula 2. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 1, characterized in that: Said Satisfy the following formula: Among them, P G is the power of the upper power feeder.

3. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 1, characterized in that: The economic optimization goal is:

4. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 3 is characterized in that: The scheduling scheme is determined by the following steps: The economic optimization objective is converted into the minimum optimization objective as follows: The constraints satisfied by the minimum optimization objective are: Among them, T out is the ambient temperature; The augmented Lagrangian function of the air source heat pump system participating in the peak load regulation of the power grid is constructed as shown in the following formula: Among them, λ is the Lagrange multiplier corresponding to the constraint condition, and ρ is the penalty function term factor corresponding to the constraint condition; Iteratively optimize based on the following formula and T, until the optimization result meets the preset end condition: At the end of the iterative optimization As the power setting value of the air source heat pump system in each peak regulation cycle.

5. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 4, characterized in that: The use of the scheduling scheme to control the air source heat pump system to participate in the peak load regulation of the power grid specifically includes the following steps: Determine the control rate u of the air source heat pump system in each peak load period based on the power setting value; The circulating hot water temperature of the air source heat pump system is set based on the control rate u.

6. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 5, characterized in that: Determine the control rate u of the air source heat pump system by following the steps below: Establishing a composite model of circulating hot water temperature change of the air source heat pump system, the composite model includes a building heat exchange part, an influence part of the air source heat pump load rate S, and an influence part of the indoor and outdoor temperature difference; Based on the composite model, the spatial state equation of the load rate S of the air source heat pump system is established: in, F=Pmax,y=P ashp , A ω is the volume of circulating hot water, α is the heat transfer efficiency, β is the unit conversion parameter, Pmax, P ashp are the maximum operating power and actual power of the air source heat pump system, cop is the energy efficiency coefficient, kin is the specific heat coefficient, T set Set temperature for circulating hot water; The sliding surface m(t) with the load rate S of the air source heat pump system as the sliding parameter is constructed based on the following formula: in, is the power setting value of the air source heat pump system corresponding to the current time t in the scheduling scheme, and e(t) is the power tracking error; The control rate u of the air source heat pump system is determined based on the following formula: Among them, ε and k are sliding mode control coefficients.

7. The method for power grid peak load regulation of an air source heat pump system based on economic optimization objectives according to claim 6, characterized in that: The circulating hot water temperature of the air source heat pump system is set based on the control rate u, specifically, The circulating hot water temperature set value Tset,t of the air source heat pump system at the current time t is determined based on the following formula:

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