Cluster air conditioner collaborative control method and apparatus, and storage medium

Abstract

Provided are a cluster air conditioner collaborative control method and apparatus, and a storage medium. The method comprises: under the constraint of a peak regulation market and a carbon market, obtaining the total income of an air conditioner aggregator on the basis of a peak regulation income of a peak regulation response stage, a rebound fluctuation reward and punishment income of a rebound optimization stage, an income of the carbon market and the total compensation cost of an air conditioner user; by using the maximization of the total income of the air conditioner aggregator as an objective, constructing a cluster air conditioner collaborative control model oriented to two stages of peak regulation response-rebound optimization; using a particle swarm algorithm to obtain an optimal solution of the cluster air conditioner collaborative control model to serve as an optimal temperature regulation strategy of cluster air conditioners; and performing collaborative control on all the regulatable cluster air conditioners on the basis of the optimal temperature regulation strategy of the cluster air conditioners.

Description

A cluster air conditioning collaborative control method, device and storage medium 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, building a new power system with new energy as the main body is a necessary path to achieve the major national strategic goals of carbon peak and carbon neutrality. The carbon emissions market is a new area that is currently attracting much attention, but at this stage, domestic and foreign scholars are mainly focusing 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.

[0005] Summary of the Invention

[0006] 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.

[0007] In order to achieve the above objectives / solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0008] In a first aspect, the present invention provides a cluster air conditioner collaborative control method, comprising the following steps:

[0009] Obtain the temperature information of all adjustable cluster air conditioners and calculate the total operating power of all adjustable cluster air conditioners;

[0010] 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.

[0011] 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;

[0012] 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.

[0013] 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.

[0014] 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;

[0015] All adjustable cluster air conditioners are collaboratively controlled according to the optimal temperature adjustment strategy of the cluster air conditioner.

[0016] 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:

[0017] 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;

[0018] According to the logic that each group contains J cluster air conditioners, the cluster air conditioners in the cluster air conditioner queue are divided into I air conditioner groups;

[0019] 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 P of the jth air conditioner in the i-th air conditioner group at time t is i,j,t The calculation formula is as follows:

[0020] Among them, C i,j is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, is the indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at time t, η i,j is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group, T tout is the outdoor temperature at time t, R i,j is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, and 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, P i,j,(t-△t) is the operating power of the jth air conditioner in the i-th air conditioner group at time (t-△t), where △t is the time step;

[0021] The total operating power is obtained based on the operating power of each cluster air conditioner:

[0022] Among them, P t agg is the total operating power of I air-conditioning groups managed by the air-conditioning aggregator at time t, P i,t is the operating power of the i-th air-conditioning group at time t.

[0023] 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:

[0024] 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:

[0025] Among them, △P t grid is the peak-shaving difference power after the air-conditioning aggregator adjusts the peak-shaving response phase at time t, is the total operating power of cluster air conditioners at time t during the peak load response phase, P t grid is the peak-shaving baseline of the cluster air conditioner at time t during the peak-shaving response phase, T1 is the start time of the peak-shaving response phase, and T2 is the end time of the peak-shaving response phase;

[0026] 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;

[0027] 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:

[0028] Among them, B grid is the peak-shaving revenue of the air-conditioning aggregator during the peak-shaving response phase, is the peak-shaving incentive price at time t in the peak-shaving response stage, and △t is the time step.

[0029] Combined with the first aspect, further, if △P t grid ≥0, the peak regulation is successful within the day. If △P t grid <0 means intraday peak regulation is unsuccessful;

[0030] The peak load incentive price is expressed as follows:

[0031] in, is the preset peak load success price, It is the preset peak-shaving shortage price.

[0032] 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:

[0033] The rebound baseline includes a rebound reward baseline and a rebound penalty baseline;

[0034] 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:

[0035] Among them, △P t bou P1 is the rebound difference power at time t during the rebound optimization phase. bou The rebound reward baseline, is the total operating power of the cluster air conditioner at time t during the rebound optimization phase;

[0036] 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:

[0037] in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, ρ inc,bou is the preset rebound reward price, and They are the preset first and second rebound penalty prices respectively. Penalty baseline for rebound;

[0038] 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:

[0039] Among them, S bouis the rebound volatility reward and punishment income, T2 is the starting time of the rebound optimization stage, and T3 is the end time of the rebound optimization stage.

[0040] 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:

[0041] Calculate the carbon market benefits B in the two stages of peak load response and rebound optimization based on carbon market information carb , the calculation formula is as follows:

[0042] Among them, ρ carb is the intraday carbon emission market price, For peak response-rebound optimization, the carbon quota owned by air conditioning aggregators in the two phases, E carb The total carbon emissions in the two phases of peak response-rebound optimization, λ t The dynamic carbon emission factor at time t in the two-stage peak response-rebound optimization, λ t is a known time variable, P t agg is the total operating power of the cluster air conditioner at time t, △t is the time step, T1 is the starting time of the peak load response phase, and T3 is the ending time of the rebound optimization phase;

[0043] Calculate the total compensation cost C given by the air conditioning aggregator to users in the two-stage peak-shaving response-rebound optimization based on the peak-shaving compensation price com , the calculation formula is as follows:

[0044] in, is the temperature adjustment compensation price of the jth air conditioner in the i-th air conditioner group, P i,j,t is the operating power of the jth air conditioner in the i-th air conditioner group at time t, I is the total number of cluster air conditioner groups, and J is the number of cluster air conditioners in each air conditioner group;

[0045] 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: B = B grid +B carb +S bou -C com

[0046] Among them, B grid is the peak regulation benefit in the peak regulation response phase, S bou It is the rebound volatility reward and punishment income in the rebound optimization stage.

[0047] In combination with the first aspect, further, the peak load compensation price The expression is:

[0048] Among them, △T i,j,t is the temperature change of the jth air conditioner in the i-th air conditioner group at time t, These are the first, second and third compensation price values ​​set on the grid side respectively.

[0049] Combined with the first aspect, the objective function of the cluster air conditioning collaborative control model for the two-stage peak load response and rebound optimization is: max B = B grid +B carb +S bou -C com

[0050] The constraints of the cluster air conditioning collaborative control model include:

[0051] (1) Temperature adjustment range constraints:

[0052] 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, △T i,j,t is the temperature change of the jth air conditioner in the i-th air conditioner group at time t and They are The upper and lower boundaries of

[0053] (2) Single-period carbon emission limit constraint: λ t P t agg ≤E s,max ,t∈[T1,T3]

[0054] Among them, E s,max The upper limit of carbon emissions in a single period;

[0055] (3) Full-time quota constraints:

[0056] (4) Single air conditioning balance constraints:

[0057] in, is the rated power of the jth air conditioner in the i-th air conditioner group.

[0058] In a second aspect, the present invention provides a cluster air conditioning collaborative control device, comprising:

[0059] 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;

[0060] 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;

[0061] 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;

[0062] 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.

[0063] 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;

[0064] 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.

[0065] 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.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 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. Under the premise of ensuring that the rebound is within a controllable range, 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, 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

[0068] FIG1 is a schematic diagram showing the steps of a cluster air conditioning collaborative control method according to an embodiment of the present invention;

[0069] FIG2 is a schematic diagram of a two-stage collaborative control architecture for cluster air conditioners under multi-market constraints according to an embodiment of the present invention;

[0070] FIG3 is a schematic diagram showing a curve of peak load compensation price and rebound fluctuation reward and penalty price when cluster air conditioners participate in intraday regulation in an embodiment of the present invention;

[0071] FIG4 is a schematic diagram showing the change of the aggregate power of the cluster air conditioner over time under the optimal temperature control strategy in an embodiment of the present invention;

[0072] FIG5 is a schematic diagram showing the relationship between the number of cluster air conditioners controlled, the temperature adjustment strategy, and time during the peak load response phase according to an embodiment of the present invention;

[0073] FIG6 is a schematic diagram showing the relationship between the number of cluster air conditioners controlled, the temperature adjustment strategy, and time during the rebound optimization phase according to an embodiment of the present invention;

[0074] FIG7 is a schematic structural diagram of a cluster air-conditioning cooperative control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] 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.

[0076] As shown in Figure 2, the two-stage collaborative control architecture of cluster air conditioners under multi-market constraints mainly consists of three layers:

[0077] 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.

[0078] The present invention performs collaborative control of cluster air conditioners in two stages, wherein stage 1 is the peak-shaving response stage (time period is T1-T2), and stage 2 is the rebound optimization stage (time period is T2-T3), where T1, T2, and T3 are preset time values. In stage 1, the air conditioner aggregator calls the cluster air conditioner as a demand response resource, and regulates its aggregated peak-shaving total power to below the peak-shaving baseline during the peak-shaving period. The air conditioner aggregator's income mainly comes from two aspects. First, the power grid company calculates and trades the cluster air conditioner peak-shaving income based on the peak-shaving effect (peak-shaving difference power and the corresponding peak-shaving incentive price); second, the air conditioner aggregator calculates the carbon emissions during the peak-shaving period based on the peak-shaving difference power, participates in the carbon emission trading market to obtain carbon emission income; both are included in the objective function. In stage 2, the power grid sets a rebound fluctuation reward and punishment constraint, requiring the air conditioner aggregator to regulate the aggregated rebound total power to below the corresponding rebound baseline during the rebound period, calculate the reward and punishment income according to the rebound fluctuation reward and punishment function, and simultaneously calculate the carbon emission market income.

[0079] 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.

[0080] Example 1

[0081] This embodiment introduces a cluster air conditioning collaborative control method that considers rebound fluctuation rewards and penalties under multi-market constraints, as shown in FIG1 , and specifically includes the following steps:

[0082] 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.

[0083] Step A01: Sort cluster air conditioners in ascending order based on their initial set temperatures to create an ordered cluster air conditioner queue. The initial set temperature is the temperature set by the user before temperature adjustment. Based on the logic that each group contains J (j∈{1,2,…,J}) cluster air conditioners, the cluster air conditioners in the cluster air conditioner queue are divided into I (i∈{1,2,…,I}) air conditioner groups. The maximum number of air conditioners that an air conditioner aggregator can aggregate is N=I*J.

[0084] 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:

[0085] Among them, C i,j is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, in kWh / ℃; is the indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at time t, in °C; η i,j is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group; P i,j,t is the operating power of the jth air conditioner in the i-th air conditioner group at time t, in kW; T t out is the outdoor temperature at time t, in °C; R i,j is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, in °C / kW.

[0086] In the embodiment of the present invention, the operating power P i,j,t The calculation formula is:

[0087] in, and are the upper and lower bounds of the temperature set point of the jth air conditioner in the i-th air conditioner group, in °C; is the rated power of the jth air conditioner in the i-th air conditioner group, in kW; P i,j,(t-△t) is the operating power of the jth air conditioner in the i-th air conditioner group at the previous moment ((t-△t)), in kW; △t is the time step.

[0088] and The calculation formula is as follows:

[0089] 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, unit is ℃; δ i,j 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.

[0090] 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:

[0091] Among them, P t agg is the total operating power of I air-conditioning groups managed by the air-conditioning aggregator at time t, P i,t is the operating power of the i-th air-conditioning group at time t.

[0092] 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.

[0093] 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.

[0094] Step B01, as shown in FIG3 , calculates the total operating power of the cluster air conditioners during the peak-shaving response phase based on the aggregated air conditioner power model. Calculates the peak-shaving difference power after regulation by the air conditioner aggregator 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:

[0095] Among them, △P t grid is the peak-shaving difference power after the air-conditioning aggregator adjusts the peak-shaving response phase at time t, is the total operating power of cluster air conditioners at time t during the peak load response phase, P t grid It is the peak-shaving baseline of cluster air conditioners at time t during the peak-shaving response phase.

[0096] 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.

[0097] If △P t grid ≥0 means the peak adjustment is successful within the day, and the preset peak adjustment success price is used. As the peak-shaving incentive price to calculate the peak-shaving benefits; if △P t grid <0 means that the peak load regulation is unsuccessful during the day and the preset peak load shortage price is required 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.

[0098] The expression of peak load incentive price is as follows:

[0099] in, is the peak-shaving incentive price at time t during the peak-shaving response phase.

[0100] 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:

[0101] Among them, B grid It is the peak-shaving revenue of the air-conditioning aggregator during the peak-shaving response phase.

[0102] Step B04: For the user side, a peak-shaving compensation price related to the temperature adjustment range is designed. The formula is as follows:

[0103] in, is the temperature adjustment compensation price of the jth air conditioner in the i-th air conditioner group, △T i,j,t is the temperature change of the jth air conditioner in the i-th air conditioner group at time t, These are the first, second and third compensation price values ​​set on the grid side respectively.

[0104] 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 1, 2, and 3 degrees Celsius, respectively. Three compensation prices to meet

[0105] In subsequent operations, the compensation cost of air-conditioning users can be calculated based on the peak-shaving compensation price.

[0106] Step C: Under the two-stage collaborative control architecture of cluster air conditioners under multi-market constraints, in 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. In the present invention, the rebound baseline includes a rebound reward baseline and a rebound penalty baseline. Based on the total operating power of the cluster air conditioners during the rebound optimization phase, air conditioner aggregators participate in the peak-shaving market by smoothing rebound behavior and receive rebound rewards or rebound penalties. Therefore, the rebound fluctuation reward and penalty benefits include rebound benefits or penalties, as shown in Figure 3.

[0107] 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:

[0108] Among them, △P t bou is the rebound difference power at time t during the rebound optimization phase, P1 bou The rebound reward baseline, is the total operating power of the cluster air conditioner at time t during the rebound optimization phase.

[0109] 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:

[0110] in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, ρ inc,bou 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.

[0111] According to formula (12), the rebound fluctuation reward and penalty price in the present 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 P1 bou , that is, △P t bou ≥0, the air conditioning aggregator uses the price ρ inc,bou 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 aggregate power exceeds the rebound penalty baseline P2 bou When air conditioning aggregators offer higher prices Pay rebound penalty to the grid side.

[0112] 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:

[0113] Among them, S bou In the rebound optimization stage, the hole aggregators obtain rebound benefits or penalties by participating in the peak-shaving market through rebound-smoothing behavior.

[0114] 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.

[0115] 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: B = B grid +B carb +Sbou -C com (14)

[0116] Among them, B carb The total carbon emission income of air-conditioning aggregators participating in the carbon emission market in the two-stage peak response-rebound optimization is C com Optimize the total cost of compensation given by air conditioning aggregators to users in two-stage peak shaving response-rebound.

[0117] Calculate B based on carbon market information carb , the calculation formula is:

[0118] Among them, E carb The total carbon emissions in the two phases of peak response-rebound optimization, λ t The dynamic carbon emission factor at time t in the two-stage peak response-rebound optimization, λ t is a known time variable, △t is the time step, ρ carb is the intraday carbon emission market price, Optimize the carbon quota owned by air conditioning aggregators in two phases for peak response-rebound.

[0119] Calculate C based on the peak load compensation price com , the calculation formula is:

[0120] 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.

[0121] The objective function of the cluster air conditioning collaborative control model is as follows: max B=B grid +B carb +S bou -C com (17)

[0122] The constraints of the cluster air conditioning coordinated control model include:

[0123] (1) Temperature adjustment range constraints:

[0124] in, is the temperature set point of the jth air conditioner in the i-th air conditioner group after adjustment, and They are In this embodiment of the present invention, if the air-conditioning user agrees to adjust the temperature, △T i,j,tIt can be 1℃, 2℃ or 3℃. If the air conditioner user does not agree to the temperature adjustment, △T i,j,t Take 0℃.

[0125] (2) Single-period carbon emission limit constraint: λ t P t agg ≤E s,max ,t∈[T1,T3] (20)

[0126] Among them, E s,max It is the upper limit of carbon emissions in a single period.

[0127] (3) Full-time quota constraints:

[0128] (4) Single air conditioning balance constraints:

[0129] 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, namely 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.

[0130] 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 for 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 according to 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.

[0131] 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.

[0132] Step F: Coordinately control all adjustable cluster air conditioners according to the optimal temperature control strategy obtained in step E.

[0133] In order to verify the effect of the method of the present invention, the following experiments were performed in this embodiment:

[0134] 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.

[0135] Table 1

[0136] 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.

[0137] Table 2

[0138] Step 3: Based on the information in Tables 1 and 2, a cluster air conditioning collaborative control model is constructed using the method of the present invention, and the particle swarm algorithm is used to obtain the optimal solution of the cluster air conditioning collaborative control model. Then, a reasonable group air conditioning response temperature adjustment strategy for the peak shaving response phase and a group air conditioning rebound temperature adjustment strategy for the rebound optimization phase are formulated. The results are shown in Figure 4. In Figure 4, the trend of air conditioning aggregation power is opposite to the trend of the dynamic carbon emission factor. In the peak shaving response phase, while ensuring peak shaving profits, it can bring lower carbon emissions, that is, more carbon market benefits are obtained. In the rebound optimization phase, the rebound fluctuation is large due to the return of air conditioning demand, and rebound penalties are mainly used. However, a small amount of rebound fluctuation rewards are obtained in this model. The income of air conditioning aggregators in the experiment of the present invention is shown in Table 3 below, among which the total peak shaving income of air conditioning aggregators is 8414.66 yuan.

[0139] Table 3

[0140] During the peak-shaving response phase, the number of air conditioners controlled and the cluster air conditioner temperature control strategy (amplitude) at each moment are shown in Figure 5. As can be seen from Figure 5, in the first 15 minutes, the number of air conditioners controlled was the highest, reaching 2,300, of which 1,500 air conditioners implemented a temperature control strategy of +1°C and 800 air conditioners implemented a temperature control strategy of +2°C. The second air conditioner control began at 10:45, with 50 air conditioners controlled, all implementing a temperature control strategy of +2°C. Subsequently, the air conditioner aggregator conducted cluster air conditioner control every 15 minutes, all implementing a temperature control strategy of +2°C, with the number of air conditioners ranging from 50 to 450. Control was suspended for 45 minutes at 12:15; control resumed at 13:15, with 50 air conditioners controlled, all implementing a temperature control strategy of +2°C, until the end of Phase 1 to 14:00.

[0141] During the rebound optimization phase, the number of air conditioners controlled and the cluster air conditioner temperature control strategy (amplitude) at each moment are shown in Figure 6. As can be seen from Figure 6, the first 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 -1°C temperature control strategy and 600 air conditioners implemented the -2°C temperature control strategy. The second air conditioner control began at 2:15 PM, with 750 air conditioners controlled, all of which implemented the -2°C temperature control strategy. Subsequently, the air conditioner aggregator conducted cluster air conditioner control every 15 minutes, all of which implemented the +2°C temperature control strategy. The number of air conditioners ranged from 200 to 600 until the end of Phase 2.

[0142] Example 2

[0143] Based on the same inventive concept as Example 1, this example introduces a cluster air-conditioning collaborative control device, as shown in FIG7 , including an operating power calculation module, a peak-shaving response module, a rebound optimization module, a model building module, a temperature control strategy module and a collaborative control module.

[0144] 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.

[0145] 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.

[0146] Example 3

[0147] 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.

[0148] 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.

[0149] 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; In 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 in the peak load response phase; In the rebound optimization stage, the rebound fluctuation reward and penalty benefits in 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; Under the constraints of the peak-shaving market and the carbon market, the total revenue of the air-conditioning aggregator is obtained according to 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; With the goal of maximizing 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 of the cluster air conditioning; All adjustable cluster air conditioners are collaboratively controlled according to the optimal temperature control 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 the logic that each group contains J cluster air conditioners, the cluster air conditioners in the cluster air conditioner queue are divided into I air conditioner 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 P of the jth air conditioner in the i-th air conditioner group at time t is i,j,t The calculation formula is as follows: Among them, C i,j is the equivalent heat capacity of the room where the jth air conditioner in the i-th air conditioner group is located, is the indoor temperature of the room where the jth air conditioner in the i-th air conditioner group is located at time t, η i,j is the energy efficiency ratio of the jth air conditioner in the i-th air conditioner group, T t out is the outdoor temperature at time t, R i,j is the equivalent thermal resistance of the jth air conditioner in the i-th air conditioner group, and are the upper and lower boundaries of the temperature setting 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, P i,j,(t-△t) is the operating power of the jth air conditioner in the i-th air conditioner group at time (t-△t), where △t is the time step; The total operating power is obtained according to the operating power of each cluster air conditioner: Among them, P t agg is the total operating power of I air conditioner groups managed by the air conditioner aggregator at time t, P i,t is the operating power of the i-th air-conditioning group at time t.

3. The cluster air conditioning collaborative control method according to claim 1, characterized in that: In 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 in 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 the peak-shaving response phase. The calculation formula is as follows: Among them, △P t grid is the peak load difference power after the air conditioning aggregator adjusts the peak load response phase at time t, is the total operating power of cluster air conditioners at time t during the peak load response phase, P t grid is the peak-shaving baseline of the cluster air conditioner at time t in the peak-shaving response phase, T1 is the start time of the peak-shaving response phase, and T2 is the end time of the peak-shaving 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 benefits obtained by air-conditioning aggregators participating in the peak-shaving market during the peak-shaving response phase are calculated based on the peak-shaving difference power and the peak-shaving incentive price. The calculation formula is as follows: Among them, B grid is the peak load-shaving revenue of the air-conditioning aggregator during the peak load-shaving response phase, is the peak-shaving incentive price at time t in the peak-shaving response stage, and △t is the time step.

4. The cluster air conditioning coordinated control method according to claim 3, characterized in that: If △P t grid ≥0, the peak load regulation is successful within the day. If △P t grid <0 means 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 load shortage price.

5. The cluster air conditioning coordinated control method according to claim 1, characterized in that: 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, 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 stage. The calculation formula is as follows: Among them, △P t bou is the rebound difference power at time t during the rebound optimization phase, P1 bou The rebound reward baseline, is the total operating power of cluster air conditioners at time t during the rebound optimization phase; The rebound fluctuation reward and penalty prices are set according to the rebound baseline and the total operating power of the cluster air conditioners during the rebound optimization stage: in, is the rebound fluctuation reward and penalty price at time t during the rebound optimization phase, ρ inc,bou is the preset rebound reward price, and are the preset first and second rebound penalty prices, P2 bou For rebound Penalty baseline; 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: Among them, S bou is the rebound volatility reward and punishment income, T2 is the starting time of the rebound optimization phase, and T3 is the end time of the rebound optimization phase.

6. The cluster air conditioning coordinated 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 punishment 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 B in the two stages of peak load response and rebound optimization based on the information of the carbon market carb , the calculation formula is as follows: Among them, ρ carb is the intraday carbon emission market price, The carbon quota owned by air conditioning aggregators in the two-stage peak response-rebound optimization, E carb The total carbon emissions in the two stages of peak response-rebound optimization, λ t The dynamic carbon emission factor at time t in the two-stage peak response-rebound optimization, λ t is a known time variable, P t agg is the total operating power of the cluster air conditioner at time t, △t is the time step, T1 is the starting time of the peak load response phase, and T3 is the ending time of the rebound optimization phase; Calculate the total compensation cost C given by the air conditioning aggregator to users in the two-stage peak-shaving response-rebound optimization according to the peak-shaving compensation price com , the calculation formula is as follows: in, is the temperature adjustment compensation price of the jth air conditioner in the i-th air conditioner group, P i,j,t is the operating power of the jth air conditioner in the i-th air conditioner group at time t, 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: B=B grid +B carb +S bou -C com Among them, B grid is the peak load benefit in the peak load response phase, S bou It is the rebound volatility reward and punishment income in the rebound optimization stage.

7. The cluster air conditioning coordinated control method according to claim 6, characterized in that: The peak load compensation price The expression is: Among them, △T i,j,t is the temperature change of the jth air conditioner in the i-th air conditioner group at time t, 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 peak load response and rebound optimization is: max B = B grid +B carb +S bou -C com 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 adjusted, △T i,j,t is the temperature change of the jth air conditioner in the i-th air conditioner group at time t and They are The upper and lower boundaries of (2) Single-period carbon emission restrictions: λ t P t agg ≤E s,max ,t∈[T1,T3] Among them, E s,max The upper limit of carbon emissions in a single period; (3) Full-time quota constraints: (4) Single air conditioning balance constraints: 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 load response module is used to calculate the peak load benefit of the peak load response phase according to the peak load baseline and the total operating power of the cluster air conditioners in the peak load response phase during the peak load response phase; The rebound optimization module is used to calculate the rebound fluctuation reward and penalty benefits in the rebound optimization stage according to the rebound baseline and the total operating power of the cluster air conditioners in the rebound optimization stage; The model building module is used to obtain 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 punishment revenue in the rebound optimization phase, the carbon market revenue and the total compensation cost of air conditioner users under the constraints of the peak-shaving market and the carbon market; with the goal of maximizing the total revenue 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 obtain the optimal solution of the cluster air conditioning collaborative control model using the particle swarm algorithm as the optimal temperature control strategy for 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.

Images