Air source heat pump grid peak shaving method based on capacity puzzle

Abstract

The application provides an air source heat pump power grid peak shaving method based on capacity puzzle, which executes the following steps in each peak shaving period: determining an air source heat pump cluster participating in peak shaving based on a cluster evaluation index and issuing a cluster peak shaving task to the air source heat pump cluster; the air source heat pump cluster participating in peak shaving determines a cluster adjustment amount of the current peak shaving period according to the received cluster peak shaving task, if the cluster adjustment amount is greater than the sum of the adjustable capacities of all air source heat pump heating systems contained in the cluster, the cluster schedules all air source heat pump heating systems contained in the cluster to perform power grid peak shaving in the current peak shaving period according to the respective adjustable capacities, otherwise, the cluster schedules each air source heat pump heating system contained in the cluster to perform power grid peak shaving in the current peak shaving period based on capacity puzzle. The power grid peak shaving method provided by the application performs double-layer optimization scheduling on the response of air source heat pump load participating in power system peak shaving based on the capacity puzzle idea, and can accurately track the load curve.

Description

Technical Field

[0001] This application belongs to the field of new energy and power grid control technology, specifically, it provides a power grid peak shaving method based on capacity mosaic. Background Technology

[0002] The process of air source heat pump clusters, which consist of air source heat pump heating systems, participating in power system peak shaving response generally involves reporting their response capacity to the power system the day before the response. The power system will comprehensively consider factors such as "source, grid, storage, and load" and issue load curves to each air source heat pump aggregator. On the day of the response, the aggregator needs to regulate the electricity consumption of the heating systems under its jurisdiction according to the allocated load curve. Therefore, the participation of air source heat pump clusters in power system peak shaving generally includes allocating peak shaving tasks to different air source heat pump clusters based on the cluster peak shaving capacity reported by different aggregators, and the process of aggregators optimizing the scheduling of each air source heat pump system in the cluster based on the received peak shaving tasks.

[0003] Currently, there has been considerable research on the optimization and control of air source heat pumps at the power distribution network level. For example, some control methods address the significant impact of temperature changes and adjustment frequency of electric heat pumps on normal user operation, proposing load regulation strategies that consider user comfort and fairness. Furthermore, other methods optimize the thermal comfort, economy, and energy efficiency of the heating system, using a combination of the Hooke-Jeeves algorithm and particle swarm optimization to derive the optimal operating time of the air source heat pump under a time-based control strategy. For instance, patent CN113606750A proposes a method and system for optimizing the allocation of peak-shaving control commands for air source heat pump loads. This method aims to minimize the total temperature change of the loads under the jurisdiction of the air source heat pump aggregation layer, constructing an objective function to obtain an optimized air source heat pump load scheduling model. Based on the established model, it determines the buildings and number of air source heat pump loads that need to be scheduled and controls the corresponding air source heat pump loads to be turned on or off.

[0004] The aforementioned research on control strategies for air source heat pump heating systems mainly focuses on improving user heating comfort and optimizing equipment operation, lacking research on how to issue appropriate control commands to heating stations at the power grid level to meet the economic operation of the power grid. In addition, currently, domestic air source heat pump aggregators generally distribute the assigned workload equally among each heating system under their jurisdiction, but this allocation method is not scientific and cannot take into account the response capabilities of each heating system. Summary of the Invention

[0005] To address the problems existing in the prior art, this application proposes a capacity mosaic-based peak-shaving method for air-source heat pump power grids. This method targets air-source heat pump clusters distributed in different spatial locations and performs two-level optimized scheduling of air-source heat pump loads participating in power system peak-shaving response based on the capacity mosaic concept. In the control process, multiple index calculations and task allocations are required between air-source heat pump clusters, as well as multiple sortings between heating systems. Task allocation between heating system clusters is performed according to cluster evaluation indicators, and heating stations respond according to the order of indicator weights. Scheduling is carried out according to the proposed peak-shaving response control method to achieve the effect of accurately tracking the load curve.

[0006] The embodiments of this application can be implemented through the following technical solutions:

[0007] A capacity mosaic-based method for grid peak shaving using air-source heat pumps is provided for controlling multiple air-source heat pump clusters to perform grid peak shaving. Each air-source heat pump cluster includes multiple air-source heat pump heating systems. The method executes the following steps in each peak shaving cycle:

[0008] S1, based on the cluster peak shaving capacity index, determines the air source heat pump clusters that participate in peak shaving and issues cluster peak shaving tasks to them;

[0009] S2, the air source heat pump cluster participating in peak shaving determines the cluster adjustment amount for this peak shaving cycle based on the received cluster peak shaving task. If the cluster adjustment amount is greater than the sum of the adjustable capacities of all the air source heat pump heating systems it contains, then all the air source heat pump heating systems it contains are scheduled to perform grid peak shaving for this peak shaving cycle according to their respective adjustable capacities. Otherwise, each air source heat pump heating system it contains is scheduled to perform grid peak shaving for this peak shaving cycle based on the capacity mosaic.

[0010] Preferably, the cluster adjustment amount includes an upward adjustment amount or a downward adjustment amount; and the adjustable capacity of each air source heat pump heating system includes an upward adjustable capacity and a downward adjustable capacity.

[0011] Furthermore, the adjustable capacity of each air source heat pump heating system is determined based on the following formula:

[0012] W adjust =P adjust ·t adjust ,

[0013] Among them, W adjust P adjust t adjust These represent the adjustable capacity, adjustable power, and adjustable time of the air source heat pump grid peak shaving method.

[0014] Furthermore, the t adjustThe determination is based on the predicted temperature changes of the heating space of the air source heat pump heating system.

[0015] Furthermore, the cluster peak-shaving capacity index i of any air source heat pump cluster cluster Specifically:

[0016] i cluster =λ N N n +λ P P n +λαα n ,

[0017] Where, N n P n α n These are the quantity index of the heating system of the air source heat pump cluster, the adjustable power index of the cluster, and the heat pump load importance index, respectively. N , λ P and λ α These are the weighting coefficients.

[0018] Furthermore, the N n Specifically:

[0019]

[0020] Where n is the number of air source heat pump heating systems included in the air source heat pump cluster. max n represents the maximum number of air source heat pump heating systems contained in each air source heat pump cluster. min This represents the minimum number of air source heat pump heating systems contained in each air source heat pump cluster.

[0021] The P n Specifically:

[0022]

[0023] in, This represents the average adjustable power of the air source heat pump cluster. This represents the maximum average adjustable power of each air source heat pump cluster. This represents the minimum average adjustable power of each air source heat pump cluster.

[0024] The α n Specifically:

[0025]

[0026] Where, ∑P adjust P is the sum of the adjustable power of all air source heat pump heating systems included in the air source heat pump cluster.all This represents the total power of all electrical loads in the area where the air source heat pump cluster is located.

[0027] Furthermore, in step S2, the air source heat pump cluster participating in peak shaving performs grid peak shaving for the current peak shaving cycle based on capacity mosaic scheduling of its individual air source heat pump heating systems, specifically including the following steps:

[0028] S21: Set the start and end times of the proposed scheduling cycle to the start and end times of this peak-shaving cycle;

[0029] S22: Put all t adjust Air source heat pump heating systems with a duration τ longer than the proposed scheduling period are considered as alternative heating systems for participating in grid peak shaving within the proposed scheduling period.

[0030] S23: Calculate the sum of the adjustable power of all alternative heating systems during the proposed scheduling period.

[0031] S24: If If the power requirements for this peak-shaving cycle are met, then the peak-shaving capacity index i of each alternative heating system will be used. ASHP Select the air source heat pump heating system that participates in grid peak shaving within the proposed scheduling period and assign it grid peak shaving tasks; otherwise, subtract the step size Δτ from the duration τ of the proposed scheduling period and reset it to the duration τ of the proposed scheduling period and reset the end time of the proposed scheduling period, and then return to execute step S22.

[0032] S25: If the end time of the proposed scheduling cycle reaches the end time of this peak shaving cycle, then end the power grid peak shaving of this peak shaving cycle; otherwise, reset the end time of the proposed scheduling cycle to the start time of the proposed scheduling cycle, reset the end time of this peak shaving cycle to the end time of the proposed scheduling cycle, and then return to step S22.

[0033] Furthermore, the system peak-shaving capacity index i of any air source heat pump heating system ASHP Specifically:

[0034]

[0035] Among them, β1, β2, β3, and β4 are the response level index, controllability index, participation level index, and load importance index of the air source heat pump heating system, respectively. These are the weighting coefficients.

[0036] Furthermore, β1 specifically refers to:

[0037]

[0038] Among them, Pmax This is the maximum power of the air source heat pump heating system;

[0039] Specifically, β2 refers to:

[0040]

[0041] Specifically, β3 refers to:

[0042]

[0043] Specifically, β4 refers to:

[0044]

[0045] Preferably, the The importance level is determined based on β1 to β4.

[0046] The embodiment of this application provides a capacity mosaic-based peak shaving method for air source heat pump power grids. Based on the quantitative design of the peak shaving capacity of air source heat pump heating systems, it targets a two-layer evaluation index for air source heat pump clusters and heating systems within the cluster. Within the cluster, it schedules each air source heat pump heating system based on the capacity mosaic concept, thereby adjusting the power in segments during a peak shaving cycle, and ultimately scientifically and effectively completing the peak shaving task of the air source heat pump cluster. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the architecture of multiple air source heat pump clusters participating in power grid peak shaving according to an embodiment of this application;

[0048] Figure 2 This is a flowchart of a grid peak shaving method based on capacity mosaic of an air source heat pump according to an embodiment of this application;

[0049] Figure 3 This is a schematic diagram of adjustable capacity mosaic based on the adjustable power and adjustable time of an air source heat pump heating system.

[0050] Figure 4 This is a flowchart illustrating the scheduling process of an air source heat pump cluster based on capacity mosaic according to an embodiment of this application.

[0051] Figure 5 A schematic diagram showing the determination of the duration of the scheduled period and the air source heat pump heating system participating in the scheduling at the start of a peak-shaving period, according to some embodiments.

[0052] Figure 6 The simulation results show the power grid peak shaving performance of an air source heat pump cluster under the power load curve downsampling condition according to Embodiment 1 of this application.

[0053] Figure 7The tracking error curve is the simulation result under the condition of power load curve reduction according to Embodiment 1 of this application;

[0054] Figure 8 This is a histogram of the relative error frequency distribution of the simulation results under the power load curve downscaling condition according to Embodiment 1 of this application;

[0055] Figure 9 The simulation results show the power grid peak shaving performance of an air source heat pump cluster under the power load curve upward adjustment condition according to Embodiment 1 of this application;

[0056] Figure 10 The tracking error curve is the simulation result of the power load curve being adjusted upward according to Embodiment 1 of this application;

[0057] Figure 11 This is a histogram of the relative error frequency distribution of the simulation results under the condition of power load curve reduction according to Embodiment 1 of this application. Detailed Implementation

[0058] The present application will now be further described based on preferred embodiments and with reference to the accompanying drawings.

[0059] This application provides a grid peak-shaving method based on capacity mosaicking for air source heat pumps. This method is used to control multiple air source heat pump clusters for grid peak shaving. Each air source heat pump cluster can be divided according to region or aggregator, and each air source heat pump cluster can include multiple air source heat pump heating systems. Figure 1 The diagram illustrates an architecture in which multiple air-source heat pump clusters participate in grid peak shaving, as shown in a specific embodiment.

[0060] like Figure 1 As shown, air source heat pump heating systems are installed in a dispersed manner, and due to differences in geographical location, external environment, and user terminal heat dissipation conditions, the characteristics of air source heat pump heating systems vary. Issuing control commands to each system individually would greatly increase the difficulty of control. Therefore, in the embodiments of this application, air source heat pump heating systems with similar properties within a certain area can be grouped into one cluster based on geographical location, and air source heat pump heating systems in different areas can be divided into different clusters (e.g., Figure 1 Air source heat pump cluster i and air source heat pump cluster j in the example.

[0061] When multiple air source heat pump clusters participate in the peak-shaving response of the power system, the power sector will formulate a day-ahead dispatch plan in advance and determine the load reduction tasks for all loads based on the adjustability of air source heat pump load clusters in different regions. Then, based on the evaluation indicators of each air source heat pump load cluster, the peak-shaving task weight of the cluster is determined and the peak-shaving task is allocated to each air source heat pump cluster. The task allocation between air source heat pump clusters in different regions is executed first. After each air source heat pump cluster receives the peak-shaving task instruction, it will then regulate the air source heat pump load within its jurisdiction in turn to complete the regulation task.

[0062] The difficulty in allocating grid peak-shaving tasks for air source heat pumps lies in the fact that different air source heat pump clusters have different regulation characteristics due to factors such as their geographical location and the number of air source heat pumps. At the same time, the ability of an air source heat pump heating system within the same cluster to respond to peak-shaving commands and adjust power load is not only related to its own maximum power, but also limited by conditions such as the temperature of its internal heat medium (usually circulating hot water) and indoor air temperature. Therefore, it is not as simple as adjusting the power of ordinary power loads to achieve peak-shaving commands.

[0063] It is evident that when a cluster of multiple air-source heat pump heating systems participates in grid peak shaving, the response characteristics of each individual heating system and the cluster of multiple systems need to be comprehensively considered in order to achieve a more precise allocation of peak shaving tasks. To this end, this application provides an air-source heat pump grid peak shaving method based on capacity mosaic. Figure 2 A flowchart illustrating the method in some embodiments is shown, such as Figure 2 As shown, the method performs the following steps in each peak-shaving cycle:

[0064] S1, based on the cluster peak shaving capacity index, determines the air source heat pump clusters that participate in peak shaving and issues cluster peak shaving tasks to them;

[0065] S2, the air source heat pump cluster participating in peak shaving determines the cluster adjustment amount for the current peak shaving cycle based on the received cluster peak shaving task. If the cluster adjustment amount is greater than the sum of the adjustable capacities of all its included air source heat pump heating systems, then all its included air source heat pump heating systems are scheduled to perform grid peak shaving for the current peak shaving cycle according to their respective adjustable capacities. Otherwise, based on capacity mapping, each included air source heat pump heating system is scheduled to perform grid peak shaving for the current peak shaving cycle. The above method is repeated in each peak shaving cycle until grid peak shaving for all peak shaving cycles is completed. Generally, in actual grid operation, the power system dispatching department formulates and allocates peak shaving tasks in 15-minute peak shaving cycles, monitors and collects grid operation data, and summarizes and analyzes the execution status of peak shaving tasks. The method is described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] like Figure 2 As shown, the method determines the air source heat pump heating cluster and its peak-shaving tasks through step S1, and determines and schedules the air source heat pump heating system within the air source heat pump cluster through step S2. As mentioned above, allocating and executing peak-shaving tasks at these two levels requires comprehensive consideration of the response characteristics of each air source heat pump heating system and the response capability characteristics of different clusters. Therefore, in the embodiments of this application, the peak-shaving capabilities of the air source heat pump heating system and the air source heat pump cluster are quantified respectively, and a two-level peak-shaving capability evaluation index for the air source heat pump heating system and the air source heat pump cluster is established on this basis.

[0067] A. Quantify the peak-shaving capacity of air source heat pump heating systems and air source heat pump clusters.

[0068] Before regulating an air source heat pump heating system, its adjustability needs to be quantified. To assess the adjustability of an air source heat pump heating system, embodiments of this application use an adjustable power P for any given air source heat pump heating system. adjust Adjustable time t adjust And adjustable capacity W adjust The three indicators are used to quantify adjustability, and their specific relationships are as follows:

[0069] W adjust =P adjust ·t adjust (1),

[0070] Among them, W adjust For the adjustable capacity of the air source heat pump heating system, P adjust For air source heat pump heating systems with adjustable power, t adjust To follow P adjust The maximum duration that an air source heat pump heating system can maintain during power adjustment.

[0071] The above formula reflects that, for an air source heat pump heating system, its overall peak-shaving capacity is determined by the power output and the duration for which it can operate at that power continuously. Since the power consumption of the air source heat pump heating system is in the heating of the space, therefore, t adjust The size depends on the system in P adjust Temperature changes in its open-air space during operation.

[0072] In some embodiments, the adjustable capacity of an air source heat pump heating system may include an upward adjustable capacity and a downward adjustable capacity. Specifically, at any time during the actual operation of the system, the upward (power increase) and downward (power decrease) adjustable power can be determined according to the current actual power of the system, and the temperature change of the air-heated space and the time to reach the set upper and lower temperature thresholds can be predicted based on this, thereby obtaining its upward and downward adjustable capacity.

[0073] Furthermore, it is easy to understand that for an air source heat pump cluster consisting of multiple air source heat pump heating systems, the upward adjustment amount and downward adjustment amount of the cluster can be calculated at any time and used as the quantitative value of the peak-shaving capacity of the air source heat pump cluster.

[0074] B. Establish peak-shaving capacity indicators for air source heat pump heating systems.

[0075] To scientifically and effectively select heating systems to participate in power system peak shaving, this application proposes a ranking index for the regulation capacity of air source heat pump heating systems. The index evaluates air source heat pump heating systems from four aspects: responsiveness, controllability, participation, and load importance. After an air source heat pump cluster receives a peak shaving task, the peak shaving capacity index of each air source heat pump heating system within the cluster is calculated and ranked. Heating systems can then be selected to participate in power regulation according to the index value, thereby scientifically and effectively completing the peak shaving task.

[0076] The specific indicators and definitions are as follows:

[0077] Response level index β1:

[0078] Heating systems of different sizes may have the same adjustable power in the same task, but the cost they pay to achieve this power is different. When the same amount of power is changed, the larger the heating system, the less affected it is. In order to fully ensure the normal operation of the system, the user should be as unaware of the changes as possible during the adjustment process, so a response level indicator is introduced.

[0079] The adjustable power P of the heating system before and after participating in the response adjust Its maximum power P max The ratio is defined as the responsiveness index of the system, and the definition of the responsiveness index β1 is:

[0080]

[0081] Controllability index β2:

[0082] When the power grid cluster issues a control command to the heating system, the heating system may not be able to accurately participate in grid peak shaving as instructed due to control measures or other reasons. The more times the heating system accurately responds to the peak shaving command issued by the cluster, the more reliable the heating system is. A controllability index β2 is introduced, defined as follows:

[0083]

[0084] Participation level index β3:

[0085] In a heating system cluster, the heating system with better responsiveness may always be prioritized for peak-shaving response. However, the control frequency of each heating system during cluster operation should not be too high. Heating systems that participate in control less frequently should be selected as much as possible, ensuring that every heating system can participate in peak-shaving response. Therefore, a participation index β3 is introduced, defined as follows:

[0086]

[0087] Load importance index β4:

[0088] In power systems, the same type of load can be classified into three load levels based on the different operating characteristics of its location. Similarly, air source heat pump heating systems are also identified as loads of different importance levels based on the different operating characteristics of the buildings they heat. Therefore, a load importance index β4 is introduced, defined as follows:

[0089]

[0090] System peak shaving capacity index i ASHP :

[0091] The four indicators mentioned above have different degrees of impact on the system's peak-shaving capacity. Therefore, in some preferred embodiments, r is used to describe the importance of different indicators. The load importance indicator is set as Level 1 (r4=1), the response indicator as Level 2 (r1=2), and the controllability and participation indicators as Level 3 (r2, r3=3). The expression for the lower-level weighted indicators can be obtained as follows:

[0092]

[0093] In the formula, λ i u Let be the weight of the peak-shaving capacity index of the i-th air source heat pump heating system, and its expression is:

[0094]

[0095] Peak-shaving capacity index i of the above-mentioned air source heat pump heating system ASHPThis allows us to determine the priority order for adjusting heating systems within the same cluster, thereby assisting in the rapid and effective completion of power regulation tasks.

[0096] C. Establish peak-shaving capacity indicators for air source heat pump clusters.

[0097] In the embodiments of this application, based on the actual operation of the air source heat pump heating system, the peak-shaving capacity index of the air source heat pump cluster is formulated. The peak-shaving capacity index of the air source heat pump cluster is determined from the heating system quantity index, cluster adjustable power index, and heat pump load importance index of the air source heat pump heating system cluster.

[0098] Quantity index N of heating system n :

[0099] The number of heating systems directly affects the overall peak-shaving capacity of an air-source heat pump cluster within a given area. Therefore, the number of heating systems is introduced as an index. The more heating systems in the cluster, the greater the adjustability of each individual system, resulting in a larger total adjustable power and a greater response capability. Thus, the heating system quantity index N is used. n Defined as:

[0100]

[0101] Where n is the number of air source heat pump heating systems included in the air source heat pump cluster. max n represents the maximum number of air source heat pump heating systems contained in each air source heat pump cluster. min This represents the minimum number of air source heat pump heating systems contained in each air source heat pump cluster.

[0102] Cluster adjustable power index P n :

[0103] The adjustable power of a cluster directly reflects the power response capability of an air source heat pump heating system cluster. The larger the adjustable power, the fewer heating systems are needed to complete the same power command. Therefore, the adjustable power index P of the cluster is... n Defined as:

[0104]

[0105] in, This represents the average adjustable power of the air source heat pump cluster. This represents the maximum average adjustable power of each air source heat pump cluster. This represents the minimum average adjustable power of each air source heat pump cluster.

[0106] Heat pump load importance index α n :

[0107] Nodes in a power system are interconnected due to circuit flow and power variations. Air source heat pump load regulation can alter the load at a particular node, thus changing its voltage. If the air source heat pump load adjustment is too large at any given moment, it can easily cause voltage instability at that node, potentially leading to equipment damage, grid accidents, and ultimately affecting the stability and security of the entire power system. Therefore, a regional heat pump load importance index is defined. Heat pump load importance represents the proportion of the total load that a node can adjust to its total load power. Its specific expression is:

[0108]

[0109] Where P all Let α be the total power of all electrical loads in the area where the air source heat pump cluster is located. Equation (10) reflects the proportion of the adjustable power of the air source heat pump cluster to the total power of the electrical load in its area. If the proportion of the node air source heat pump load adjustment to the total power of the node is higher, the risk of node voltage exceeding the limit caused by adjusting the air source heat pump load is higher. n The smaller.

[0110] Cluster peak shaving capability index i cluster :

[0111] Based on the magnitude of the impact of the above indicators on the cluster response capability, and assigning levels to the cluster adjustable power index and the heat pump load importance index, and setting the heating system quantity index as level two, the expression for the upper-level cluster evaluation index can be obtained:

[0112] i cluster =λ N N n +λ P P n +λ α α n (11),

[0113] Where, λ N , λ P , and λ are respectively N n P n α n The weighting coefficient can be determined using the same method as the weighting coefficient for the peak-shaving capacity index of the heating system.

[0114] The two levels of peak-shaving capacity indicators mentioned above are used for the allocation and scheduling of peak-shaving tasks in steps S1 and S2, respectively.

[0115] Specifically, in step S1, the power system dispatching department can determine the ranking and task allocation weight of each air source heat pump cluster among all clusters based on the cluster's peak-shaving capacity index, and assign i... cluster As the basis for task allocation among clusters, the task allocation standard i for the heating system cluster can be obtained. cluster_n :

[0116]

[0117]

[0118] in Assign weights to the tasks of the k-th air source heat pump cluster within the overall cluster. This represents the peak-shaving capacity index of the k-th air source heat pump cluster.

[0119] The peak-shaving task instructions issued by the power system dispatching department to each air source heat pump cluster are load instruction curves. As described above, a peak-shaving cycle is generally 15 minutes. The load power instruction within this cycle is a fixed power index. Connecting the load instructions of each peak-shaving cycle forms the load instruction curve. When an air source heat pump cluster receives a peak-shaving task corresponding to a peak-shaving cycle, it dispatches each air source heat pump heating system contained in the cluster within the peak-shaving cycle through step S2 to make it track the load instruction curve as accurately as possible.

[0120] As mentioned above, the ability of an air source heat pump heating system to respond to peak shaving commands differs from that of ordinary electrical loads. It is constrained by both adjustable power and the maximum operating time at that adjustable power. Therefore, within a peak shaving cycle, the adjustable power of the systems participating in peak shaving and the overall system changes continuously over time. In order to fully utilize the response capability of each heating system and provide a basis for the cluster's control process of tracking the load curve, in each peak shaving cycle of this embodiment, the air source heat pump cluster schedules multiple air source heat pump heating systems within the cluster based on the capacity mosaic concept.

[0121] Specifically, such as Figure 3 As shown, in the process of peak-shaving response regulation, the capacity that needs to be adjusted on the load curve is used as the target for selecting the heating system, and the total adjustable capacity of the cluster is used as the basis for judging whether it can complete the scheduling target. The adjustable capacity of the heating system is regarded as a rectangular jigsaw puzzle piece with time as the length and power as the width. It is pieced together in the two dimensions of time and power to complete the peak-shaving response in a coordinated manner.

[0122] Upon receiving a peak-shaving task, each air-source heat pump cluster can calculate the required cluster adjustment amount for the current peak-shaving cycle based on the power command and the peak-shaving cycle length. Then, by combining the cluster adjustment amount with the sum of the adjustable capacities of all heating systems within the cluster, it determines whether it can complete the peak-shaving task for the current cycle. Based on the concept of capacity mosaicking, as long as the total adjustable capacity of the cluster can meet the capacity change requirements of the current peak-shaving cycle, the cluster will definitely be able to complete the corresponding task. Therefore, if the capacity requirements of the current peak-shaving cycle cannot be met, all adjustable capacities of all heating systems are used for the peak-shaving task. If the capacity requirements can be met, the current peak-shaving cycle is further divided into multiple planned scheduling cycles based on the capacity mosaicking concept, and the optimal heating system is selected to perform the peak-shaving task in each scheduling cycle.

[0123] Specifically, such as Figure 4 As shown, capacity mosaic-based scheduling includes the following steps:

[0124] S21: Set the start and end times of the proposed scheduling cycle to the start and end times of this peak-shaving cycle;

[0125] S22: Put all t adjust Air source heat pump heating systems with a duration τ longer than the proposed scheduling period are considered as alternative heating systems for participating in grid peak shaving within the proposed scheduling period.

[0126] S23: Calculate the sum of the adjustable power of all alternative heating systems during the proposed scheduling period.

[0127] S24: If If the power requirements for this peak-shaving cycle are met, then the peak-shaving capacity index i of each alternative heating system will be used. ASHP Select the air source heat pump heating system that participates in grid peak shaving within the proposed scheduling period and assign it grid peak shaving tasks; otherwise, subtract the step size Δτ from the duration τ of the proposed scheduling period and reset it to the duration τ of the proposed scheduling period and reset the end time of the proposed scheduling period, and then return to execute step S22.

[0128] S25: If the end time of the proposed scheduling cycle reaches the end time of this peak shaving cycle, then end the power grid peak shaving of this peak shaving cycle; otherwise, reset the end time of the proposed scheduling cycle to the start time of the proposed scheduling cycle, reset the end time of this peak shaving cycle to the end time of the proposed scheduling cycle, and then return to step S22.

[0129] Figure 5 This diagram illustrates, in a specific embodiment, how an air source heat pump cluster determines a proposed scheduling cycle based on the concept of capacity mosaicking, as shown below. Figure 5As shown, the duration τ of the cluster's proposed scheduling cycle is first set to τ0 = 15 min, with its start and end times being the start and end times of this peak-shaving cycle, respectively.

[0130] All heating systems within this cluster with adjustable time greater than or equal to τ are capable of participating in this scheduling, with adjustable time t... adjust The heating systems with output >τ are marked as alternative heating systems participating in this scheduling. The sum of their adjustable power is defined as the total adjustable power P within the cluster that can satisfy τ. cluster (τ), its expression is:

[0131]

[0132] The proposed adjustable power of the cluster is calculated according to formula (16) and compared with the received power command. If the power command is not met, τ decreases in steps of Δτ = 1 min until the standby heating system marked by the cluster can complete the power command of this scheduling cycle.

[0133] Next, the selected alternative heating systems will be put into grid peak shaving in sequence according to their peak shaving capacity indicators, until the end of the planned scheduling cycle is reached, while the aggregate power P of the cluster is updated. ASHP And the total adjustable capacity (including adjustable power and adjustable time).

[0134] If the end of the planned scheduling cycle coincides with the end of the current peak-shaving cycle, then the current peak-shaving operation will end; otherwise, it will... Figure 5 The end time of the proposed scheduling period is reset to the start time of the new proposed scheduling cycle, and the end time of the current peak shaving cycle is set to the end time of the new proposed scheduling cycle. Then, the process returns to step S22 to determine the duration of the new proposed scheduling cycle and the heating systems participating in grid peak shaving. Obviously, when determining the duration of the new proposed scheduling cycle and the heating systems, since the aggregated power of the cluster and the adjustable capacity of each heating system have changed at the end of the previous proposed scheduling cycle, it is necessary to recalculate the adjustable capacity of each heating system.

[0135] Example 1.

[0136] This embodiment uses the aforementioned air source heat pump grid peak shaving method based on capacity mosaic to simulate grid peak shaving. It is assumed that the peak shaving period on a certain day starts at 12 noon and lasts for 150 minutes.

[0137] First, the adjustable power and adjustable time of the air source heat pump heating system are obtained, and the ranking index of the control capability of the air source heat pump heating system and the evaluation index of the adjustable capability of the cluster are calculated. Then, the cluster control scheme of the heating system and the load curve are used as the initial conditions for the peak shaving response simulation to simulate the power grid peak shaving process. The simulation parameters are shown in Table 1.

[0138] Table 1 Key parameters for example analysis

[0139]

[0140] Figure 6 The simulation results of an air-source heat pump cluster performing grid peak shaving under the condition of power load curve down are shown. Figure 7 , Figure 8 The corresponding tracking error curves and relative error frequency distribution histograms are shown respectively; Figure 9 The simulation results of an air-source heat pump cluster performing grid peak shaving under the condition of upward adjustment of the power load curve are shown. Figure 10 , Figure 11 The corresponding tracking error curves and relative error frequency distribution histograms are shown respectively.

[0141] pass Figures 6 to 11 As can be seen, the method proposed in this application can track the load curve well. The root mean square error for cluster load reduction is 0.093MW, with an average relative error of 0.096%, while the root mean square error for cluster load increase is 0.089MW, with an average relative error of 0.079%. The relative error of the tracking is mostly distributed between 0% and 0.1%, which verifies the feasibility of air source heat pumps participating in peak shaving response and the effectiveness of the above method.

[0142] The specific embodiments of this application have been described in detail above. For those skilled in the art, several improvements and modifications can be made to this application without departing from the principle of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A grid peak-shaving method for air-source heat pumps based on capacity mosaicking, used to control multiple air-source heat pump clusters for grid peak shaving, each air-source heat pump cluster including multiple air-source heat pump heating systems, characterized in that, Perform the following steps during each peak shaving cycle: S1, based on the cluster peak shaving capacity index, determines the air source heat pump clusters that participate in peak shaving and issues cluster peak shaving tasks to them; S2, the air source heat pump cluster participating in peak shaving determines the cluster adjustment amount for this peak shaving cycle based on the received cluster peak shaving task. If the cluster adjustment amount is greater than the sum of the adjustable capacities of all the air source heat pump heating systems it contains, then all the air source heat pump heating systems it contains are scheduled to perform grid peak shaving for this peak shaving cycle according to their respective adjustable capacities. Otherwise, each air source heat pump heating system it contains is scheduled to perform grid peak shaving for this peak shaving cycle based on the capacity mosaic. Cluster peak-shaving capacity index of any air source heat pump cluster Specifically: ， in, , , These are the indicators for the number of heating systems in the air source heat pump cluster, the cluster's adjustable power, and the importance of the heat pump load. , ,and These are the weighting coefficients; The Specifically: ， in, This refers to the number of air source heat pump heating systems included in the air source heat pump cluster. This represents the maximum number of air source heat pump heating systems contained in each air source heat pump cluster. This represents the minimum number of air source heat pump heating systems contained in each air source heat pump cluster. The Specifically: ， in, This represents the average adjustable power of the air source heat pump cluster. This represents the maximum average adjustable power of each air source heat pump cluster. This represents the minimum average adjustable power of each air source heat pump cluster. The Specifically: ， in, This refers to the sum of the adjustable power of all air source heat pump heating systems included in the air source heat pump cluster. This represents the total power of all electrical loads in the area where the air source heat pump cluster is located.

2. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 1, characterized in that: The cluster adjustment amount includes an upward adjustment of the cluster or a downward adjustment of the cluster; and The adjustable capacity of each air source heat pump heating system includes both upward and downward adjustable capacity.

3. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 1, characterized in that, The adjustable capacity of each air source heat pump heating system is determined based on the following formula: ， in, , , These represent the adjustable capacity, adjustable power, and adjustable time of the air source heat pump grid peak shaving method.

4. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 3, characterized in that: The The determination is based on the predicted temperature changes of the heating space of the air source heat pump heating system.

5. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 3, characterized in that, In step S2, the air source heat pump cluster participating in peak shaving performs grid peak shaving for the current peak shaving cycle based on capacity mosaic scheduling of its individual air source heat pump heating systems. This specifically includes the following steps: S21: Set the start and end times of the proposed scheduling cycle to the start and end times of this peak-shaving cycle; S22: All Duration longer than the planned scheduling period The air source heat pump heating system is used as an alternative heating system to participate in grid peak shaving during the proposed scheduling cycle. S23: Calculate the sum of the adjustable power of all alternative heating systems during the proposed scheduling period. ; S24: If To meet the power requirements for this peak-shaving cycle, the peak-shaving capacity of each alternative heating system will be considered. Select the air-source heat pump heating systems that will participate in grid peak shaving within the proposed scheduling period and assign them grid peak shaving tasks; otherwise, adjust the duration of the proposed scheduling period. Subtract step size Then reset to the duration of the proposed scheduling period. And reset the end time of the proposed scheduling period, and then return to execute step S22; S25: If the end time of the proposed scheduling cycle reaches the end time of this peak shaving cycle, then end the power grid peak shaving of this peak shaving cycle; otherwise, reset the end time of the proposed scheduling cycle to the start time of the proposed scheduling cycle, reset the end time of this peak shaving cycle to the end time of the proposed scheduling cycle, and then return to step S22.

6. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 5, characterized in that, System peak-shaving capacity index of any air source heat pump heating system Specifically: ， in, , , , These are the response level index, controllability index, participation level index, and load importance index of the air source heat pump heating system. , , , These are the weighting coefficients.

7. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 6, characterized in that, The Specifically: ， in, This is the maximum power of the air source heat pump heating system; The Specifically: ； The Specifically: ； The Specifically: 。 8. The air-source heat pump grid peak-shaving method based on capacity mosaicking according to claim 6, characterized in that: The ~ based on ~ The importance level is determined.

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