A cluster air conditioning collaborative control method, device and storage medium

Abstract

The present invention provides a cluster air conditioner collaborative control method, device, and storage medium. The method comprises: under the constraints of the peak-shaving market and the carbon market, calculating the total revenue of the air conditioner aggregator based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and penalty revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost for air conditioner users; constructing a cluster air conditioner collaborative control model for the two-stage peak-shaving response and rebound optimization phases with the goal of maximizing the total revenue of the air conditioner aggregator; utilizing a particle swarm algorithm to obtain the optimal solution of the cluster air conditioner collaborative control model, which serves as the optimal temperature control strategy for the cluster air conditioner; and collaboratively controlling all controllable cluster air conditioners according to the optimal temperature control strategy for the cluster air conditioner. The present invention can meet the peak-shaving needs of the power grid by optimizing the temperature control strategy of the grouped air conditioners and changing the cluster air conditioner power consumption curve, effectively reducing the carbon emissions of the cluster air conditioners and fully leveraging the regulatory role of the massive air conditioner load resources on the user side.

Description

Technical Field

[0001] The present invention relates to a cluster air conditioning collaborative control method, device and storage medium taking into account rebound fluctuation rewards and penalties under multi-market constraints, and belongs to the technical field of cluster air conditioning control. Background Art

[0002] In recent years, air conditioning equipment, with its thermal energy storage, temperature controllability, and high load share, has become a high-quality, flexible load resource. The emergence of air conditioning aggregators has further enhanced the competitiveness of air conditioning equipment in demand response. Through effective demand management, clustered air conditioning systems can not only quickly respond to dispatch demands, alleviating power supply constraints, but also mitigate the rebound effects of load fluctuations, further ensuring the safe operation of the power grid.

[0003] Currently, cluster air conditioner load regulation has been incorporated into normal power system operations. By rationally controlling the air conditioner load temperature setpoints, load reduction can be achieved at a low cost, enabling participation in the peak-shaving market and alleviating supply-demand imbalances. Due to the large number of air conditioners and their widely varying conditions, power companies struggle to directly access their aggregated power and conduct regulation. They typically rely on air conditioner aggregators to participate in peak-shaving. However, after a short-term peak reduction during a demand response event, the disorderly return of air conditioner setpoint temperatures can cause load spikes after the control is removed, significantly impacting the power grid.

[0004] In addition, the carbon emissions market is a new area that is currently attracting much attention, but at this stage, domestic and foreign scholars' research mainly focuses on the quota allocation system of various industries and the construction of carbon trading markets; cluster air-conditioning carbon emissions have a scale effect, and currently there is no corresponding model or decision-making research on the control of carbon emissions in the cluster air-conditioning regulation process. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the present invention proposes a cluster air-conditioning collaborative control method, device and storage medium that considers rebound fluctuation rewards and penalties under multi-market constraints. The air-conditioning control stage is divided into two stages: peak-shaving response and rebound optimization. The two-stage temperature control strategy of the cluster air-conditioning is optimized through an effective reward and punishment strategy, and the peak-shaving market constraints and carbon trading market constraints are simultaneously met, so as to achieve the effects of efficient peak-shaving, rebound suppression and carbon emission reduction.

[0006] In order to achieve the above objectives / solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0007] In a first aspect, the present invention provides a cluster air conditioner collaborative control method, comprising the following steps:

[0008] Obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners;

[0009] During the peak-shaving response phase, the peak-shaving benefit of the peak-shaving response phase is calculated based on the peak-shaving baseline and the total operating power of the cluster air conditioners during the peak-shaving response phase.

[0010] During the rebound optimization phase, the rebound fluctuation reward and penalty benefits of the rebound optimization phase are calculated based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase;

[0011] Under the constraints of the peak-shaving market and the carbon market, the total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and punishment revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users.

[0012] Aiming to maximize the total revenue of air conditioning aggregators, a cluster air conditioning collaborative control model for peak load response and rebound optimization is constructed.

[0013] The particle swarm algorithm is used to obtain the optimal solution of the cluster air conditioning collaborative control model as the optimal temperature control strategy for the cluster air conditioning;

[0014] All adjustable cluster air conditioners are collaboratively controlled according to the optimal temperature adjustment strategy of the cluster air conditioner.

[0015] In combination with the first aspect, further, obtaining the temperature information of all adjustable cluster air conditioners and calculating the total operating power of all adjustable cluster air conditioners includes:

[0016] According to the initial set temperature of each adjustable cluster air conditioner, all adjustable cluster air conditioners are sorted in order from low to high to obtain an ordered cluster air conditioner queue;

[0017] According to each group contains The logic of cluster air conditioners divides the cluster air conditioners in the cluster air conditioner queue into air conditioning groups;

[0018] According to the thermal dynamic process of the room where the cluster air conditioner is located, the operating power of each cluster air conditioner is obtained. The operating power of the jth air conditioner in the i-th air conditioner group at the moment The calculation formula is as follows:

[0019]

[0020]

[0021] in, is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, for The indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at the moment, is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group, for The outdoor temperature at the time, is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, are the upper and lower bounds of the temperature set point of the jth air conditioner in the i-th air conditioner group, is the rated power of the jth air conditioner in the i-th air conditioner group, for The operating power of the jth air conditioner in the i-th air conditioner group at the moment, is the time step;

[0022] The total operating power is obtained based on the operating power of each cluster air conditioner:

[0023]

[0024]

[0025] in, for The total operating power of I air conditioner group managed by the air conditioner aggregator at this moment, for The operating power of the i-th air conditioning group at time.

[0026] In combination with the first aspect, further, during the peak shaving response phase, the peak shaving benefit of the peak shaving response phase is calculated based on the peak shaving baseline and the total operating power of the cluster air conditioners during the peak shaving response phase, including:

[0027] The peak-shaving difference power after regulation by the air-conditioning aggregator is calculated based on the total operating power of the cluster air conditioners during the peak-shaving baseline and peak-shaving response phases. The calculation formula is as follows:

[0028]

[0029] in, During the peak load response phase The peak load difference power after the air conditioning aggregator adjusts the power at that time. During the peak load response phase The total operating power of the cluster air conditioner at all times, During the peak load response phase The peak load baseline of cluster air conditioning at all times, is the starting time of the peak load response phase, The end time of the peak load response period;

[0030] Determine whether the peak-shaving is successful within the day based on the peak-shaving difference power, and set the corresponding peak-shaving incentive price;

[0031] The peak-shaving revenue obtained by air-conditioning aggregators participating in the peak-shaving market during the peak-shaving response phase is calculated based on the peak-shaving difference power and the peak-shaving incentive price. The calculation formula is as follows:

[0032]

[0033] in, is the peak-shaving revenue of the air-conditioning aggregator during the peak-shaving response phase, During the peak load response phase The peak-shaving incentive price at the moment, is the time step.

[0034] Combined with the first aspect, further, if If the peak load regulation is successful, Then the intraday peak regulation is unsuccessful;

[0035] The peak load incentive price is expressed as follows:

[0036]

[0037] in, is the preset peak load success price, It is the preset peak-shaving shortage price.

[0038] Combined with the first aspect, further, in the rebound optimization stage, the rebound fluctuation reward and penalty benefits of the rebound optimization stage are calculated based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization stage, including:

[0039] The rebound baseline includes a rebound reward baseline and a rebound penalty baseline;

[0040] The rebound difference power is calculated based on the rebound bonus baseline and the total operating power of the cluster air conditioners during the rebound optimization phase. The calculation formula is as follows:

[0041]

[0042] in, is the rebound difference power at time t during the rebound optimization phase, The rebound reward baseline, is the total operating power of the cluster air conditioner at time t during the rebound optimization phase;

[0043] The rebound fluctuation reward and penalty prices are set based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase:

[0044]

[0045] in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, is the preset rebound reward price, and They are the preset first and second rebound penalty prices respectively. Penalty baseline for rebound;

[0046] The rebound volatility bonus and penalty income is calculated based on the rebound difference power and the rebound volatility bonus and penalty price. The calculation formula is as follows:

[0047]

[0048] in, To rebound the volatility reward and penalty income, is the starting moment of the rebound optimization phase, This is the end moment of the rebound optimization phase.

[0049] Combined with the first aspect, under the constraints of the peak-shaving market and the carbon market, the total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and penalty revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users, including:

[0050] Calculate the carbon market benefits of the two-stage peak-shaving response-rebound optimization based on carbon market information , the calculation formula is as follows:

[0051]

[0052] in, is the intraday carbon emission market price, Optimize the carbon quotas owned by air conditioning aggregators in the two phases of peak response and rebound. To optimize the total carbon emissions in the two phases of peak response and rebound, To optimize the dynamic carbon emission factor at time t in the two stages of peak load response-rebound, is a known time variable, for The total operating power of the cluster air conditioner at all times, is the time step, is the starting time of the peak load response phase, The end moment of the rebound optimization phase;

[0053] Calculate the total compensation cost given by air conditioning aggregators to users in the two-stage peak-shaving response-rebound optimization based on the peak-shaving compensation price , the calculation formula is as follows:

[0054]

[0055] in, is the peak load compensation price of the jth air conditioner in the i-th air conditioner group, for The operating power of the jth air conditioner in the i-th air conditioner group at time t, where I is the total number of cluster air conditioner groups and J is the number of cluster air conditioners in each air conditioner group;

[0056] The total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and punishment revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users. The calculation formula is as follows:

[0057]

[0058] in, is the peak regulation benefit in the peak regulation response phase, It is the rebound volatility reward and punishment income in the rebound optimization stage.

[0059] In combination with the first aspect, further, the peak load compensation price The expression is:

[0060]

[0061] in, for The temperature change of the jth air conditioner in the i-th air conditioner group at the moment, These are the first, second and third compensation price values ​​set on the grid side respectively.

[0062] Combined with the first aspect, the objective function of the cluster air conditioning coordinated control model for the two-stage peak load response and rebound optimization is:

[0063]

[0064] The constraints of the cluster air conditioning collaborative control model include:

[0065] (1) Temperature adjustment range constraints:

[0066]

[0067]

[0068] in,

[0069] (2) Single-period carbon emission limit constraints:

[0070]

[0071] in, The upper limit of carbon emissions in a single period;

[0072] (3) Full-time quota constraints:

[0073]

[0074] (4) Balance constraints of single air conditioner:

[0075]

[0076] in, is the rated power of the jth air conditioner in the i-th air conditioner group.

[0077] In a second aspect, the present invention provides a cluster air conditioning collaborative control device, comprising:

[0078] An operating power calculation module is used to obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners;

[0079] A peak-shaving response module is used to calculate the peak-shaving benefit of the peak-shaving response phase according to the peak-shaving baseline and the total operating power of the cluster air conditioners during the peak-shaving response phase;

[0080] The rebound optimization module is used to calculate the rebound fluctuation reward and penalty benefits during the rebound optimization phase based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase;

[0081] The model building module is used to calculate the total revenue of the AC aggregator based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and penalty revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of AC users, under the constraints of the peak-shaving market and the carbon market. With the goal of maximizing the total revenue of the AC aggregator, a cluster AC collaborative control model for the peak-shaving response and rebound optimization phases is constructed.

[0082] The temperature control strategy module is used to use the particle swarm algorithm to obtain the optimal solution of the cluster air conditioning collaborative control model as the optimal temperature control strategy of the cluster air conditioning;

[0083] The collaborative control module is used to collaboratively control all adjustable cluster air conditioners according to the optimal temperature adjustment strategy of the cluster air conditioner.

[0084] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the cluster air conditioning collaborative control method of the first aspect is implemented.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] The present invention proposes a cluster air-conditioning collaborative control method, device and storage medium, which divides the air-conditioning control stage into two stages: peak shaving response and rebound optimization. Under the dual constraints of the peak shaving market and the carbon emission market, the total revenue of the air-conditioning aggregator is calculated based on the peak shaving revenue, rebound fluctuation reward and punishment revenue, carbon market revenue and air-conditioning compensation cost, and the cluster air-conditioning collaborative control model is constructed and solved with the goal of maximizing the total revenue of the air-conditioning aggregator, thereby obtaining the optimal temperature control strategy, and performing temperature control on the cluster air-conditioning in the peak shaving response stage and the rebound optimization stage respectively. The present invention can change the cluster air-conditioning power consumption curve to meet the peak shaving demand of the power grid by optimizing the temperature control strategy of the grouped air-conditioning, under the premise of ensuring that the rebound is within a controllable range, effectively reduce the carbon emissions of the cluster air-conditioning, give full play to the regulatory role of the massive air-conditioning load resources on the user side, and achieve the effects of efficient peak shaving, rebound suppression and carbon emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 Schematic diagram of the steps of the cluster air conditioner collaborative control method according to an embodiment of the present invention;

[0088] Figure 2 Schematic diagram of a two-stage collaborative control architecture for cluster air conditioners under multi-market constraints in an embodiment of the present invention;

[0089] Figure 3 It is a schematic diagram of the curve of the peak load compensation price and the rebound fluctuation reward and penalty price when the cluster air conditioner participates in the intraday regulation in an embodiment of the present invention;

[0090] Figure 4 Schematic diagram showing the change of aggregate power of cluster air conditioners over time under the optimal temperature control strategy in an embodiment of the present invention;

[0091] Figure 5 Schematic diagram showing the relationship between the number of cluster air conditioners controlled, temperature control strategy, and time during the peak load response phase in an embodiment of the present invention;

[0092] Figure 6 Schematic diagram showing the relationship between the number of cluster air conditioners controlled, temperature control strategy, and time in the rebound optimization phase in an embodiment of the present invention;

[0093] Figure 7 Shown is a structural schematic diagram of a cluster air-conditioning cooperative control device in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] It should be noted that: in order to solve the problem that after the short-term peak shaving of the demand response event, the disorderly setting temperature adjustment behavior of the air conditioner will cause a load spike after the exit control, which will cause a large impact on the power grid, the present invention incorporates the rebound stage into the total regulation period, and then obtains the price information of the intraday peak-shaving market and the carbon market through the air-conditioning aggregator, and establishes a two-stage collaborative control architecture for cluster air conditioners under multi-market constraints. This architecture involves two stages: peak-shaving response and rebound optimization.

[0095] like Figure 2 As shown in Figure 1, the two-stage collaborative control architecture of cluster air conditioners under multi-market constraints mainly includes three layers:

[0096] The first layer is the power grid side. The dispatching center formulates a daily peak-shaving plan based on the daily supply and demand curve and sends it to the air-conditioning aggregator. The daily peak-shaving plan includes peak-shaving period, peak-shaving compensation price, etc.; the second layer is the air-conditioning aggregator side. As the leader of cluster air-conditioning, the air-conditioning aggregator must not only participate in the peak-shaving market and carbon emission market, collect market information and obtain market benefits, but also formulate temperature control strategies for the group air-conditioning peak-shaving response-rebound optimization stage; the third layer is the air-conditioning side. Each individual air-conditioning under the management of the air-conditioning aggregator receives the corresponding temperature control strategy instructions and completes the control as planned.

[0097] The present invention performs coordinated control of cluster air conditioners in two stages, wherein stage 1 is the peak load response stage (time period is ), stage 2 is the rebound optimization stage (period is ), 、 、 is a preset time value. In Phase 1, AC aggregators utilize cluster AC units as demand response resources, regulating their aggregated total peak-shaving power below the peak-shaving baseline during peak-shaving periods. AC aggregators derive revenue from two main sources: first, the power grid company calculates and trades the cluster AC peak-shaving revenue based on the peak-shaving effect (peak-shaving differential power and the corresponding peak-shaving incentive price); second, AC aggregators calculate their carbon emissions during the peak-shaving period based on the peak-shaving differential power and participate in the carbon emissions trading market to earn carbon emissions revenue; both are incorporated into the objective function. In Phase 2, the power grid imposes rebound fluctuation reward and penalty constraints, requiring AC aggregators to regulate their aggregated total rebound power below the corresponding rebound baseline during the rebound period. Reward and penalty revenue is calculated based on the rebound fluctuation reward and penalty function, and carbon emissions market revenue is simultaneously calculated.

[0098] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0099] Example 1

[0100] This embodiment introduces a cluster air conditioning collaborative control method considering rebound fluctuation rewards and penalties under multi-market constraints. Figure 1 As shown, the specific steps include:

[0101] Step A: Count the number of cluster air conditioners that can participate in regulation, obtain the temperature information of all adjustable cluster air conditioners, sort and group the cluster air conditioners that can participate in regulation, build a single air conditioner power model and an aggregated air conditioner power model, and then calculate the total operating power of all adjustable cluster air conditioners.

[0102] Step A01, sort the cluster air conditioners in descending order according to the initial set temperature of the air conditioner to obtain an ordered cluster air conditioner queue, wherein the initial set temperature of the air conditioner is the temperature set by the air conditioner user before the temperature adjustment. The logic of cluster air conditioners divides the cluster air conditioners in the cluster air conditioner queue into Air conditioner groups, the maximum number of air conditioners that an air conditioner aggregator can aggregate is indivual.

[0103] Step A02: Construct a power model for a single air conditioner based on the I air conditioner group. In this embodiment of the present invention, the power model for a single air conditioner uses a first-order equivalent thermal parameter model to describe the thermal dynamics of the room to which the air conditioning load belongs. The expression for the power model for a single air conditioner is as follows:

[0104] (1)

[0105] in, is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, in kWh / ℃; for The indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at the moment, in °C; is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group; for The operating power of the jth air conditioner in the i-th air conditioner group at the moment, in kW; for The outdoor temperature at the moment, in °C; is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, in °C / kW.

[0106] In the embodiment of the present invention, the operating power The calculation formula is:

[0107] (2)

[0108] in, are the upper and lower bounds of the temperature set point of the jth air conditioner in the i-th air conditioner group, respectively, in °C; is the rated power of the jth air conditioner in the i-th air conditioner group, in kW; For the previous moment ( The operating power of the jth air conditioner in the i-th air conditioner group at time (in kW); is the time step.

[0109] The calculation formula is as follows:

[0110] (3)

[0111] (4)

[0112] in, is the temperature set point of the jth air conditioner in the i-th air conditioner group. In the initial state, Usually set manually by the air conditioner user, during the collaborative control process, Can be changed with the temperature control strategy, the unit is ℃; is the indoor temperature variation width of the room where the j-th air conditioner in the i-th air conditioner group is located, in °C.

[0113] Step A03: Obtain an aggregated air conditioner power model based on the individual air conditioner power model, and calculate the total operating power of the cluster air conditioners using the following formula:

[0114] (5)

[0115] (6)

[0116] in, for The total operating power of I air conditioner group managed by the air conditioner aggregator at this moment, for The operating power of the i-th air conditioning group at time.

[0117] Step B: Under the two-stage collaborative control architecture of cluster air conditioners under multi-market constraints, in the peak-shaving response stage, the peak-shaving benefit of the peak-shaving response stage is calculated based on the peak-shaving baseline and the total operating power of the cluster air conditioners in the peak-shaving response stage.

[0118] In the embodiment of the present invention, the peak-shaving response stage mainly involves three aspects: determining whether the intra-day peak-shaving is successful, setting the peak-shaving incentive price, and the peak-shaving compensation price on the air-conditioning side.

[0119] Step B01: Figure 3As shown in the figure, the total operating power of the cluster air conditioners during the peak-shaving response phase is calculated based on the aggregated air conditioner power model. The peak-shaving difference power after the air conditioner aggregator's regulation is calculated based on the peak-shaving baseline and the total operating power of the cluster air conditioners during the peak-shaving response phase. The formula is as follows:

[0120] (7)

[0121] in, During the peak load response phase The peak load difference power after the air conditioning aggregator adjusts the power at that time. During the peak load response phase The total operating power of the cluster air conditioner at all times, During the peak load response phase The peak-shaving baseline of cluster air conditioning at all times.

[0122] Step B02: Determine whether the intraday peak-shaving is successful based on the peak-shaving difference power after adjustment by the air-conditioning aggregator, and set the corresponding peak-shaving incentive price.

[0123] like If the peak adjustment is successful within the day, the preset peak adjustment success price Calculate the peak-shaving benefits as the peak-shaving incentive price; if If the peak load regulation is unsuccessful during the day, it is necessary to purchase the peak load shortage price in the real-time electricity market. Purchase electricity to make up for the excess power at the peak load price. The peak-shaving benefits are calculated as the peak-shaving incentive price.

[0124] The expression of peak load incentive price is as follows:

[0125] (8)

[0126] in, During the peak load response phase The peak-shaving incentive price at the moment.

[0127] Step B03: Calculate the peak-shaving revenue obtained by the air-conditioning aggregator from participating in the peak-shaving market during the peak-shaving response phase based on the peak-shaving difference power and the peak-shaving incentive price. The formula is as follows:

[0128] (9)

[0129] in, It is the peak-shaving revenue of the air-conditioning aggregator during the peak-shaving response phase.

[0130] Step B04: For the user side, a peak-shaving compensation price related to the temperature adjustment range is designed. The formula is as follows:

[0131] (10)

[0132] in, is the peak load compensation price of the jth air conditioner in the i-th air conditioner group, for The temperature change of the jth air conditioner in the i-th air conditioner group at the moment, These are the first, second and third compensation price values ​​set on the grid side respectively.

[0133] In the embodiment of the present invention, there are three temperature adjustment compensation schemes for air conditioner users to choose from. The temperature adjustment changes corresponding to the three schemes are ℃, and the corresponding Three compensation prices to meet .

[0134] In subsequent operations, the compensation cost of air-conditioning users can be calculated based on the peak-shaving compensation price.

[0135] Step C: Under the two-stage collaborative control architecture of cluster air conditioners under multi-market constraints, in the rebound optimization stage, the rebound fluctuation reward and penalty benefits of the rebound optimization stage are calculated based on the rebound baseline and the total operating power of the cluster air conditioners in the rebound optimization stage. In the present invention, the rebound baseline includes a rebound reward baseline and a rebound penalty baseline. According to the size of the total operating power of the cluster air conditioners in the rebound optimization stage, the air conditioner aggregator participates in the peak-shaving market by smoothing the rebound behavior, and will obtain a rebound reward or rebound penalty. Therefore, the rebound fluctuation reward and penalty benefits include rebound benefits or penalties, such as Figure 3 shown.

[0136] Step C01: Calculate the total operating power of the cluster air conditioners during the rebound optimization phase based on the aggregated air conditioner power model. Calculate the rebound difference power based on the rebound reward baseline and the total operating power of the cluster air conditioners during the rebound optimization phase. The formula is as follows:

[0137] (11)

[0138] in, is the rebound difference power at time t during the rebound optimization phase, The rebound reward baseline, is the total operating power of the cluster air conditioner at time t during the rebound optimization phase.

[0139] Step C02: Set the rebound fluctuation reward and penalty price based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase. The formula is as follows:

[0140] (12)

[0141] in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, is the preset rebound reward price, The present invention considers segmented penalties and sets two types of rebound penalty prices. and They are the preset first and second rebound penalty prices respectively. .

[0142] According to formula (12), the rebound fluctuation reward and penalty price in this invention is The value of is related to whether the rebound load is within the rebound fluctuation reward and penalty range. Specifically: when the aggregate power is adjusted to the rebound reward baseline ,Right now When air conditioning aggregators Obtain rebound compensation benefits in the rebound optimization phase; when the aggregated power is adjusted to between the rebound reward baseline and the rebound penalty baseline, the air conditioning aggregator will Pay rebound penalty to the grid side; when the aggregated power exceeds the rebound penalty baseline When air conditioning aggregators offer higher prices Pay rebound penalty to the grid side.

[0143] Step C03: Calculate the rebound benefit or penalty based on the rebound difference power and the rebound fluctuation reward and penalty price. The rebound fluctuation reward and penalty function is:

[0144] (13)

[0145] in, In the rebound optimization stage, the hole aggregators obtain rebound benefits or penalties by participating in the peak-shaving market through rebound-smoothing behavior.

[0146] Step C: Under the constraints of the peak-shaving market and the carbon market, a total profit calculation function for air-conditioning aggregators, including peak-shaving revenue, carbon market revenue, air-conditioning compensation costs, and rebound fluctuation reward and punishment revenue, is proposed as the target of cluster air-conditioning collaborative control under multi-market constraints.

[0147] The formula for calculating the total revenue B of the air conditioning aggregator in the two stages of peak load response and rebound optimization is:

[0148] (14)

[0149] in, To optimize the total carbon emission income of air-conditioning aggregators participating in the carbon emission market in the two-stage peak response-rebound optimization, Optimize the total cost of compensation given by air conditioning aggregators to users in two-stage peak shaving response-rebound.

[0150] Calculated based on information from the carbon market , the calculation formula is:

[0151] (15)

[0152] in, To optimize the total carbon emissions in the two phases of peak response and rebound, To optimize the dynamic carbon emission factor at time t in the two stages of peak load response-rebound, is a known time variable, is the time step, is the intraday carbon emission market price, Optimize the carbon quota owned by air conditioning aggregators in two phases for peak response-rebound.

[0153] Calculated based on the peak load compensation price , the calculation formula is:

[0154] (16)

[0155] Step D: Considering the constraints such as the control temperature range, control duration, maximum carbon emissions per period, and maximum total carbon emissions, a cluster air conditioning collaborative control model with the goal of maximizing the total revenue of the air conditioning aggregator and oriented to the two-stage peak-shaving response-rebound optimization is obtained.

[0156] The objective function of the cluster air conditioning collaborative control model is as follows:

[0157] (17)

[0158] The constraints of the cluster air conditioning coordinated control model include:

[0159] (1) Temperature adjustment range constraints:

[0160] (18)

[0161] (19)

[0162]

[0163] (2) Single-period carbon emission limit constraints:

[0164] (20)

[0165] in, It is the upper limit of carbon emissions in a single period.

[0166] (3) Full-time quota constraints:

[0167] (twenty one)

[0168] (4) Balance constraints of single air conditioner:

[0169] (twenty two)

[0170] Step E: Use the particle swarm algorithm to obtain the optimal solution of the cluster air conditioning collaborative control model, and then obtain the optimal temperature control strategy of the cluster air conditioning under multi-market constraints considering rebound fluctuation rewards and penalties. Considering that the present invention includes two stages: peak shaving response and rebound optimization, the optimal temperature control strategy can be further refined into a cluster air conditioning response temperature control strategy in the peak shaving response stage and a cluster air conditioning rebound temperature control strategy in the rebound optimization stage.

[0171] First, the original data of air-conditioning aggregators, peak-shaving markets, and carbon trading markets (including the number of controllable cluster air-conditioners, market price information, peak-shaving targets, etc.) are input, and the particle population size and initial value are set according to the original data. The position of the particles corresponds to the temperature control strategy, and the fitness function is the calculation formula of the total revenue B of the air-conditioning aggregator; in the optimization process, the fitness of each particle is calculated according to the position of the particle in each iteration, that is, the total revenue of the air-conditioning aggregator, and the local optimal solution and global optimal solution of the current iteration are obtained based on the particle fitness value; the speed and position of the particles are updated, and then the local optimal solution and global optimal solution are updated; when the relative error of the global optimal solution of the current and subsequent iterations meets the preset accuracy requirements, it can be considered that the equilibrium solution has been found, the iteration ends, and the global optimal solution of the last iteration is output as the optimal temperature control strategy.

[0172] While ensuring that the model has an equilibrium solution, due to errors in computer solutions, the iteration may not converge during the initial solution. In this case, the particle population size and initial value, as well as the feasible domain and search range, are updated to facilitate the search for an equilibrium solution.

[0173] Step F: Coordinately control all adjustable cluster air conditioners according to the optimal temperature control strategy obtained in step E.

[0174] In order to verify the effect of the method of the present invention, the following experiments were performed in this embodiment:

[0175] Step 1: 5,000 controllable cluster air conditioners sign a control incentive contract with an air conditioner aggregator. Before the implementation of demand response, each cluster air conditioner has been operating stably within its set temperature range. The relevant parameters of the cluster air conditioners under the control of the air conditioner aggregator are counted, including equivalent thermal resistance, equivalent heat capacity of the room, initial user-set temperature, air conditioner operating power, indoor temperature variation width, etc. In the experiment of this embodiment, the parameters of the cluster air conditioners are random numbers uniformly distributed within the corresponding range, as shown in Table 1 below.

[0176] Table 1

[0177]

[0178] Step 2: Collect price information of the electricity market and carbon emission market through air conditioning aggregators and participate in the electricity market according to the peak load regulation plan issued by the power grid. The specific price information and plan are shown in Table 2 below. In the experiment of this embodiment, the peak load regulation period is from 10:00 to 14:00 and the rebound period is from 14:00 to 16:00. Users who sign an agreement with the cluster air conditioner will receive temperature adjustment compensation according to the temperature adjustment range. The temperature adjustment range is 1℃, 2℃ and 3℃. The corresponding compensation price is They are 0.2, 0.4 and 0.6 yuan / kWh respectively.

[0179] Table 2

[0180]

[0181] Step 3: Based on the information in Table 1 and Table 2, a cluster air conditioning collaborative control model is constructed by the method of the present invention, and the optimal solution of the cluster air conditioning collaborative control model is obtained by using the particle swarm algorithm, and then a reasonable group air conditioning response temperature adjustment strategy in the peak response phase and a group air conditioning rebound temperature adjustment strategy in the rebound optimization phase are formulated. The results are as follows: Figure 4 shown. Figure 4 The trend in air conditioner aggregation power is opposite to the trend in the dynamic carbon emission factor. During the peak-shaving response phase, while maintaining peak-shaving profits, carbon emissions are lowered, resulting in greater carbon market benefits. During the rebound optimization phase, rebound fluctuations are significant due to the return of air conditioner demand, primarily resulting in rebound penalties. However, this model also provides a small rebound fluctuation reward. The revenue generated by the air conditioner aggregator in this experiment is shown in Table 3 below. The total peak-shaving revenue for the air conditioner aggregator is 8,414.66 yuan.

[0182] Table 3

[0183]

[0184] In the peak load response phase, the number of air conditioners controlled at each moment and the cluster air conditioner temperature adjustment strategy (amplitude) are as follows: Figure 5 As shown, according to Figure 5 It can be seen that in the first 15 minutes, the number of air conditioners regulated was the largest, reaching 2,300, of which 1,500 air conditioners implemented the +1°C temperature control strategy and 800 air conditioners implemented the +2°C temperature control strategy; the second air conditioner regulation began at 10:45, with 50 air conditioners regulated, all implementing the +2°C temperature control strategy; subsequently, the air conditioner aggregator conducted cluster air conditioner regulation every 15 minutes, all implementing the +2°C temperature control strategy, and the number of air conditioners ranged from 50 to 450; regulation was suspended for 45 minutes at 12:15; regulation resumed at 13:15, with 50 air conditioners regulated, all implementing the +2°C temperature control strategy, until the end of stage 1 to 14:00.

[0185] In the rebound optimization stage, the number of air conditioners controlled at each moment and the cluster air conditioner temperature adjustment strategy (range) are as follows: Figure 6 As shown, according to Figure 6 It can be seen that the first air conditioning control was started at 2:00 PM. At this time, the number of air conditioners controlled was the largest, reaching 2,100. Among them, 1,500 air conditioners implemented the temperature control strategy of -1°C and 600 air conditioners implemented the temperature control strategy of -2°C. The second air conditioning control began at 2:15 PM, with the number of air conditioners controlled at 750, all of which implemented the temperature control strategy of -2°C. Subsequently, the air conditioning aggregator performed cluster air conditioning control every 15 minutes, all of which implemented the temperature control strategy of +2°C. The number of air conditioners ranged from 200 to 600 until the end of stage 2.

[0186] Example 2

[0187] Based on the same inventive concept as Example 1, this embodiment introduces a cluster air conditioning collaborative control device, such as Figure 7 As shown, it includes an operating power calculation module, a peak regulation response module, a rebound optimization module, a model building module, a temperature regulation strategy module and a collaborative control module.

[0188] The operating power calculation module is used to obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners; the peak-shaving response module is used to calculate the peak-shaving benefit of the peak-shaving response phase according to the peak-shaving baseline and the total operating power of the cluster air conditioners in the peak-shaving response phase; the rebound optimization module is used to calculate the rebound fluctuation reward and punishment benefit of the rebound optimization phase according to the rebound baseline and the total operating power of the cluster air conditioners in the rebound optimization phase; the model construction module is used to obtain the total benefit of the air conditioner aggregator based on the peak-shaving benefit of the peak-shaving response phase, the rebound fluctuation reward and punishment benefit of the rebound optimization phase, the carbon market benefit and the total compensation cost of the air conditioner users under the constraints of the peak-shaving market and the carbon market; with the goal of maximizing the total benefit of the air conditioner aggregator, a cluster air conditioner collaborative control model for the two stages of peak-shaving response and rebound optimization is constructed; the temperature control strategy module is used to use the particle swarm algorithm to obtain the optimal solution of the cluster air conditioner collaborative control model as the optimal temperature control strategy of the cluster air conditioner; the collaborative control module is used to collaboratively control all adjustable cluster air conditioners according to the optimal temperature control strategy of the cluster air conditioner.

[0189] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0190] Example 3

[0191] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the cluster air conditioning collaborative control method introduced in Example 1 is implemented.

[0192] The present invention divides the air conditioning control stage into two stages: peak shaving response and rebound optimization. Under the dual constraints of the peak shaving market and the carbon emission market, the total revenue of the air conditioning aggregator is calculated based on the peak shaving revenue, rebound fluctuation reward and punishment revenue, carbon market revenue and air conditioning compensation cost, and the cluster air conditioning collaborative control model is constructed and solved with the goal of maximizing the total revenue of the air conditioning aggregator, thereby obtaining the optimal temperature control strategy, and performing temperature control on the peak shaving response stage and the rebound optimization stage respectively. In the present invention, the air conditioning aggregator faces the two stages of peak shaving response and rebound optimization, while taking into account the market demands of peak shaving rebound and carbon emission. Under the premise of ensuring that the rebound is within a controllable range, it can change the cluster air conditioning power consumption curve to meet the peak shaving demand of the power grid by optimizing the temperature control strategy of the grouped air conditioners, thereby maximizing the peak shaving revenue as much as possible, effectively reducing the carbon emissions of the cluster air conditioners, and giving full play to the regulatory role of the massive air conditioning load resources on the user side, so as to achieve the effects of efficient peak shaving, suppressing rebound and reducing carbon emissions.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0197] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A cluster air conditioning collaborative control method, characterized in that: The steps include: Obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners; During the peak-shaving response phase, the peak-shaving benefit of the peak-shaving response phase is calculated based on the peak-shaving baseline and the total operating power of the cluster air conditioners during the peak-shaving response phase. During the rebound optimization phase, the rebound fluctuation reward and penalty benefits of the rebound optimization phase are calculated based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase; Under the constraints of the peak-shaving market and the carbon market, the total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and punishment revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users. Aiming to maximize the total revenue of air conditioning aggregators, a cluster air conditioning collaborative control model for peak load response and rebound optimization is constructed. The particle swarm algorithm is used to obtain the optimal solution of the cluster air conditioning collaborative control model as the optimal temperature control strategy for the cluster air conditioning; All adjustable cluster air conditioners are collaboratively controlled according to the optimal temperature adjustment strategy of the cluster air conditioner.

2. The cluster air conditioning collaborative control method according to claim 1, characterized in that: The step of obtaining the temperature information of all adjustable cluster air conditioners and calculating the total operating power of all adjustable cluster air conditioners includes: According to the initial set temperature of each adjustable cluster air conditioner, all adjustable cluster air conditioners are sorted in order from low to high to obtain an ordered cluster air conditioner queue; According to each group contains The logic of cluster air conditioners divides the cluster air conditioners in the cluster air conditioner queue into air conditioning groups; According to the thermal dynamic process of the room where the cluster air conditioner is located, the operating power of each cluster air conditioner is obtained. The operating power of the jth air conditioner in the i-th air conditioner group at the moment The calculation formula is as follows: ； ； in, is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, for The indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at the moment, is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group, for The outdoor temperature at the time, is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, are the upper and lower bounds of the temperature set point of the jth air conditioner in the i-th air conditioner group, is the rated power of the jth air conditioner in the i-th air conditioner group, for The operating power of the jth air conditioner in the i-th air conditioner group at the moment, is the time step; The total operating power is obtained based on the operating power of each cluster air conditioner: ； ； in, for The total operating power of I air conditioner group managed by the air conditioner aggregator at this moment, for The operating power of the i-th air conditioning group at time.

3. The cluster air conditioning collaborative control method according to claim 1, characterized in that: During the peak load response phase, the peak load benefit of the peak load response phase is calculated based on the peak load baseline and the total operating power of the cluster air conditioners during the peak load response phase, including: The peak-shaving difference power after regulation by the air-conditioning aggregator is calculated based on the total operating power of the cluster air conditioners during the peak-shaving baseline and peak-shaving response phases. The calculation formula is as follows: ； in, During the peak load response phase The peak load difference power after the air conditioning aggregator adjusts the power at that time. During the peak load response phase The total operating power of the cluster air conditioner at all times, During the peak load response phase The peak load baseline of cluster air conditioning at all times, is the starting time of the peak load response phase, The end time of the peak load response period; Determine whether the peak-shaving is successful within the day based on the peak-shaving difference power, and set the corresponding peak-shaving incentive price; The peak-shaving revenue obtained by air-conditioning aggregators participating in the peak-shaving market during the peak-shaving response phase is calculated based on the peak-shaving difference power and the peak-shaving incentive price. The calculation formula is as follows: ； in, is the peak-shaving revenue of the air-conditioning aggregator during the peak-shaving response phase, During the peak load response phase The peak-shaving incentive price at the moment, is the time step.

4. The cluster air conditioning collaborative control method according to claim 3, characterized in that: like If the peak load regulation is successful, Then the intraday peak regulation is unsuccessful; The peak load incentive price is expressed as follows: ； in, is the preset peak load success price, It is the preset peak-shaving shortage price.

5. The cluster air conditioning collaborative control method according to claim 1, characterized in that: During the rebound optimization phase, the rebound fluctuation reward and penalty benefits are calculated based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase, including: The rebound baseline includes a rebound reward baseline and a rebound penalty baseline; The rebound difference power is calculated based on the rebound bonus baseline and the total operating power of the cluster air conditioners during the rebound optimization phase. The calculation formula is as follows: ； in, is the rebound difference power at time t during the rebound optimization phase, The rebound reward baseline, is the total operating power of the cluster air conditioner at time t during the rebound optimization phase; The rebound fluctuation reward and penalty prices are set based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase: ； in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, is the preset rebound reward price, and They are the preset first and second rebound penalty prices respectively. Penalty baseline for rebound; The rebound volatility bonus and penalty income is calculated based on the rebound difference power and the rebound volatility bonus and penalty price. The calculation formula is as follows: ； in, To rebound the volatility reward and penalty income, is the starting moment of the rebound optimization phase, This is the end moment of the rebound optimization phase.

6. The cluster air conditioning collaborative control method according to claim 1, characterized in that: Under the constraints of the peak-shaving market and the carbon market, the total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and penalty revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users, including: Calculate the carbon market benefits of the two-stage peak-shaving response-rebound optimization based on carbon market information , the calculation formula is as follows: ； in, is the intraday carbon emission market price, Optimize the carbon quotas owned by air conditioning aggregators in the two phases of peak response and rebound. To optimize the total carbon emissions in the two phases of peak response and rebound, To optimize the dynamic carbon emission factor at time t in the two stages of peak load response-rebound, is a known time variable, for The total operating power of the cluster air conditioner at all times, is the time step, is the starting time of the peak load response phase, The end moment of the rebound optimization phase; Calculate the total compensation cost given by air conditioning aggregators to users in the two-stage peak-shaving response-rebound optimization based on the peak-shaving compensation price , the calculation formula is as follows: ； in, is the peak load compensation price of the jth air conditioner in the i-th air conditioner group, for The operating power of the jth air conditioner in the i-th air conditioner group at time t, where I is the total number of cluster air conditioner groups and J is the number of cluster air conditioners in each air conditioner group; The total revenue of the air-conditioning aggregator is obtained based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and punishment revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of air-conditioning users. The calculation formula is as follows: ； in, is the peak regulation benefit in the peak regulation response phase, It is the rebound volatility reward and punishment income in the rebound optimization stage.

7. The cluster air conditioning collaborative control method according to claim 6, characterized in that: The peak load compensation price The expression is: ； in, for The temperature change of the jth air conditioner in the i-th air conditioner group at the moment, These are the first, second and third compensation price values ​​set on the grid side respectively.

8. The cluster air conditioning coordinated control method according to claim 6, characterized in that: The objective function of the cluster air conditioning coordinated control model for the two-stage peak load response and rebound optimization is: ； The constraints of the cluster air conditioning collaborative control model include: (1) Temperature adjustment range constraints: ； ； in, is the temperature set point of the jth air conditioner in the i-th air conditioner group after adjustment, is the temperature set point before the jth air conditioner in the i-th air conditioner group is controlled, for The temperature change of the jth air conditioner in the i-th air conditioner group at the moment and They are The upper and lower boundaries of (2) Single-period carbon emission limit constraints: ； in, The upper limit of carbon emissions in a single period; (3) Full-time quota constraints: ； (4) Balance constraints of single air conditioner: ； in, is the rated power of the jth air conditioner in the i-th air conditioner group.

9. A cluster air conditioning collaborative control device, characterized in that: include: An operating power calculation module is used to obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners; A peak-shaving response module is used to calculate the peak-shaving benefit of the peak-shaving response phase according to the peak-shaving baseline and the total operating power of the cluster air conditioners during the peak-shaving response phase; The rebound optimization module is used to calculate the rebound fluctuation reward and penalty benefits during the rebound optimization phase based on the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization phase; The model building module is used to calculate the total revenue of the AC aggregator based on the peak-shaving revenue in the peak-shaving response phase, the rebound fluctuation reward and penalty revenue in the rebound optimization phase, the carbon market revenue, and the total compensation cost of AC users, under the constraints of the peak-shaving market and the carbon market. With the goal of maximizing the total revenue of the AC aggregator, a cluster AC collaborative control model for the peak-shaving response and rebound optimization phases is constructed. The temperature control strategy module is used to use the particle swarm algorithm to obtain the optimal solution of the cluster air conditioning collaborative control model as the optimal temperature control strategy of the cluster air conditioning; The collaborative control module is used to collaboratively control all adjustable cluster air conditioners according to the optimal temperature adjustment strategy of the cluster air conditioner.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cluster air conditioning collaborative control method as described in any one of claims 1 to 8 is implemented.

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