A distribution network dispatching method considering multi-time scale air conditioning load demand response

Abstract

The invention discloses a distribution network dispatching method considering multi-time scale air conditioning load demand response, which belongs to the technical field of power system optimization operation, and solves the problem of how to optimize the demand response of air conditioning load through multi-time scale dispatching, so as to improve the operation efficiency and economy of the power grid. The invention obtains the line information of the distribution network and the air conditioning load data of the users at the distribution network nodes; constructs the constraint conditions of the distribution network operation; according to the line information of the distribution network, considers the operation characteristics of the air conditioning load under multi-time scales, and constructs the differentiated constraint conditions of the air conditioning load demand response under different time scales; constructs a multi-level objective function considering the multi-time scale air conditioning load demand response; solves the dispatching method model considering the multi-time scale air conditioning load demand response, and obtains the generalized controllable resource dispatching operation scheme of multi-stage and different time scales. The invention can effectively reduce the operation cost of the power grid, improve the reliability of the power grid, and improve the dispatching flexibility and response speed of the power grid.

Description

Technical Field

[0001] The invention belongs to the technical field of power system optimization operation, and relates to a distribution network dispatching method considering multi-time scale air conditioning load demand response. Background Art

[0002] In modern power systems, with the widespread integration of distributed renewable energy, energy storage technology, and demand-side resources, the operation of power grids faces new challenges and opportunities. In particular, as an important part of the demand side, the dispatching and management of air-conditioning loads have a significant impact on the stability and economy of the power grid. However, existing technologies often adopt a single time scale strategy when dealing with air-conditioning load dispatching, and fail to fully utilize the potential of demand response, especially when facing a power grid operating environment with multiple time scales.

[0003] Traditional grid dispatching methods, such as time-of-use (TOU) and direct load control (DLC), can achieve demand-side management to a certain extent, but these methods usually lack consideration of user response willingness and comfort, and in actual operation there are problems such as slow response speed and limited regulation capacity. In addition, when integrating distributed energy and demand response resources, existing technologies often ignore the coupling relationship and comprehensive dispatch potential of resources at different time scales.

[0004] In recent years, although some studies have begun to focus on multi-time scale demand response strategies, these studies are mostly based on theoretical models and lack in-depth discussion and empirical analysis of multi-time scale dispatch strategies in actual power grid operation. In particular, in actual power grid operation, how to coordinate price signals and incentive mechanisms at different time scales to achieve flexible dispatch of air conditioning loads is still an urgent problem to be solved. Summary of the invention

[0005] The technical solution of the present invention is used to solve the problem of how to optimize the demand response of air-conditioning loads through multi-time scale scheduling, thereby improving the operating efficiency and economy of the power grid.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] A distribution network dispatching method considering multi-time scale air conditioning load demand response includes the following steps:

[0008] Step 1: Traverse the entire distribution network and count the number of nodes K and the distance between nodes d in the network ij and branch unit impedance, and number the nodes;

[0009] Step 2: Screen and distribute the air conditioning load conditions of users at each node in the power grid, including price-based air conditioning load demand response and interruptible air conditioning load that can be scheduled on the day before, emergency air conditioning load demand response for intraday scheduling, and direct air conditioning load demand response for real-time scheduling;

[0010] Step 3: Consider the power flow constraints of the distribution network, the operation constraints of the energy storage system, the operation constraints of the distributed generation system, and the load loss constraints to determine the operation constraints of the distribution network;

[0011] Step 4: Based on the distribution network line information and considering the operation characteristics of air-conditioning loads at multiple time scales, differentiated constraints for air-conditioning load demand responses at different time scales are constructed;

[0012] Step 5: Based on the time scale response characteristics of air conditioning loads at different times, and considering the user experience and economic characteristics of the distribution network, a multi-level objective function considering the multi-time scale air conditioning load demand response is constructed;

[0013] Step 6: According to the distribution network line information, the multi-time scale air conditioning load operation characteristics and the generalized controllable resource operation characteristics, the distribution network operation constraints and the multi-time scale air conditioning load operation constraints are considered to obtain the generalized controllable resource scheduling and operation plan at multiple stages and different time scales.

[0014] Furthermore, the formula for the power flow constraint of the distribution network described in step 3 is as follows:

[0015]

[0016]

[0017]

[0018]

[0019] in, represents the set of parent nodes of node k, represents the set of parent nodes of node j, and Respectively represent the active power and reactive power on branch ij at time t, and Respectively represent the active power and reactive power on branch jk at time t, and They are the square of the current value of branch ij and the square of the voltage value of node i at time t, is the node voltage of node j at time t, and is the minimum and maximum node voltage of node j at time t, and is the voltage and reactance value of branch ij, and is the active load and reactive load of node j at time t, is the output of the energy storage system at node j at time t, is the number of energy storage systems, and is the active power and reactive power of the distributed generation at node j at time t, is the number of distributed energy resources, and is the active and reactive call amount of the air conditioning load of node j at time t when executing IL, and is the active and reactive call amount when the air conditioning load of node j executes EDR at time t, and is the active load loss and reactive load loss of node j at time t, is the maximum line transmission capacity of branch ij.

[0020] Furthermore, the formula for the energy storage system operation constraint described in step 3 is as follows:

[0021]

[0022] in, is the power of the i-th energy storage system at time t, is the power of the i-th energy storage system at time t-1, and are the lower and upper limits of the energy storage system respectively. is the charging and discharging power of the energy storage system at time t, is the upper limit of the charging and discharging power of the energy storage system, and are the charging decision variables and discharging decision variables of the energy storage system at node i at time t, and is the charge and discharge efficiency.

[0023] Furthermore, the formula of the distributed generation operation constraint described in step 3 is as follows:

[0024]

[0025]

[0026] in, and are the active and reactive outputs of the ith distributed generation at time t, is the maximum active output of the distributed generation predicted at time t, is the capacity of distributed power generation.

[0027] Furthermore, the formula of the load loss constraint described in step 3 is as follows:

[0028]

[0029] Among them, the load loss constraint Indicates that the load loss of node i at time t cannot exceed the node prediction value .

[0030] Furthermore, the method described in step 4 to construct differentiated constraint conditions for air conditioning load demand response at different time scales based on the distribution network line information and considering the operating characteristics of air conditioning load at multiple time scales is as follows:

[0031] The time scale is divided into daily, hourly and minute-level dispatching scales to adapt to demand response signals of different frequencies, corresponding to the day-ahead, intraday and real-time optimization dispatching scales respectively;

[0032] 1) Day-ahead scheduling constraints:

[0033]

[0034] 2) Intraday scheduling constraints:

[0035]

[0036] 3) Real-time scheduling constraints:

[0037]

[0038] in, is the capacity of the air conditioning load at node i at time t that complies with the price-based air conditioning load demand response, and are the minimum and maximum values ​​of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the interruptible air conditioning load, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the emergency air conditioning load demand response, is the maximum value of the air conditioning load that can be urgently adjusted by the air conditioning load aggregator. is the response capacity of the air conditioning load of node i at time t to the direct air conditioning load control demand, It is the maximum value of the air conditioning load that can be directly controlled by the air conditioning load aggregator.

[0039] Furthermore, the method described in step 5 to construct a multi-level objective function considering the multi-time scale air conditioning load demand response according to the time scale response characteristics of different air conditioning loads and considering the user experience and economic characteristics of the distribution network is as follows:

[0040] The details are as follows:

[0041] 1) Objective function of day-ahead scheduling:

[0042]

[0043]

[0044] 2) Objective function of intraday scheduling:

[0045]

[0046] 3) Objective function of real-time scheduling:

[0047]

[0048]

[0049] in, , , are the objective functions for day-ahead, intra-day, and real-time scheduling, respectively. , , Represent the cost functions of energy storage system, user load and distributed energy respectively, T represents the execution cycle time under different time scales, is the number of energy storage systems, is the energy storage system cost function, is the energy storage system maintenance cost function, Represents the depreciation cost of the energy storage system per unit charge and discharge times, and are 0-1 variables representing the charging and discharging states of the energy storage power station, N is the number of nodes containing air conditioning loads, The cost coefficient for performing IL for the air conditioning load, Cost factor for performing EDR for air conditioning load, is the cost coefficient of the user's lost load, is the call volume of the air conditioning load of node i when executing IL at time t, is the call volume when the air conditioning load of node i executes EDR at time t, is the load loss of the user at node i at time t, is the number of distributed energy resources, is the i-th distributed energy cost function at time t, is the penalty cost coefficient for abandoned light, To predict the distributed energy processing value at time t, is the output value at time t under the actual scheduling strategy;

[0050] The objective function of the intraday rolling optimization is similar to that of the day-ahead dispatch model. However, in the intraday rolling optimization stage, the PDR call volume of the air conditioning load and the demand response capacity of the IL are known, and the total load cost is changed to the sum of the EDR and DLC demand response capacities. and There is no change compared with the day-ahead dispatch model, except that the constraints on the DLC demand response capacity are added, where: is the response capacity of the air conditioning load of node i at time t to meet the IL demand, The maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator Cost factor for performing DLC ​​for air conditioning load, is the response capacity of the air conditioning load of node i at time t to meet the EDR demand, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the call amount when the air conditioning load of node i executes DLC at time t, is the active power call amount when the air conditioning load of node i executes IL at time t, is the active power call amount when the air-conditioning load at node i executes EDR at time t; in the real-time scheduling stage, the PDR response capacity, IL response capacity, and EDR response capacity of the air-conditioning load are all known, and the output behavior pattern of the energy storage system is obtained in the day-ahead optimization stage. Therefore, the related cost of the energy storage system is a known quantity and will not be used as part of the objective function to simplify the model.

[0051] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned distribution network scheduling method considering multi-time scale air conditioning load demand response, and the processor is configured to execute the program stored in the memory.

[0052] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the distribution network dispatching method considering multi-time-scale air conditioning load demand response.

[0053] The advantages of the present invention are:

[0054] (1) The method of the present invention innovatively integrates day-ahead, intraday and real-time dispatching to adapt to the dynamic changes of power grid operation. By coordinating price-based demand response (PDR) and incentive demand response (IDR), the present invention can effectively reduce the operating cost of the power grid, improve the reliability of the power grid, reduce the instability risk caused by load fluctuations, and improve the dispatch flexibility and response speed of the power grid.

[0055] (2) The method of the present invention significantly reduces the load loss of the power grid during peak hours through refined scheduling management, reduces the economic losses caused by demand fluctuations, and improves the overall economic benefits of the power grid. At the same time, it takes into account the access of distributed renewable energy and optimizes the utilization efficiency of new energy through intelligent scheduling strategies, thereby enhancing the adaptability and absorption capacity of the power grid to new energy fluctuations.

[0056] (3) The method of the present invention takes into account the user's response willingness and comfort level. Through reasonable load adjustment, it reduces the user's electricity use inconvenience and improves the user's satisfaction with the grid service. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of a distribution network dispatching method considering multi-time scale air conditioning load demand response;

[0058] Figure 2 It is an IEEE33 node distribution network topology diagram containing line and node information;

[0059] Figure 3 It is the output of each part of the 33 nodes of the distribution network participating in the coordinated regulation;

[0060] Figure 4 It is the calling status of various DR resources in the distribution network. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0063] Embodiment 1

[0064] Taking the IEEE 33-node distribution network as an example, the distribution network dispatching method considering multi-time scale air conditioning load demand response of the present invention is introduced. Figure 1As shown, the specific steps include:

[0065] Step 1: traverse the entire power distribution network, count the number of nodes in the power distribution network of this embodiment, N=33, the line numbers of the faulty lines and controllable resources, and the node numbers that are already in Figure 2 The impedance of each branch also needs to be counted.

[0066] Consider an actual power distribution network. 33 distribution network nodes are numbered using natural numbers 1, 2, ..., 33. The distance between nodes is d ij Indicates that the unit length data is obtained according to the ij branch cable model, and R ij and X ij represents the actual resistance and actual reactance between the i-th node and the j-th node, where i, j∈1,2,…,33.

[0067] Step 2: Screen and distribute the air-conditioning load conditions of users at each node in the power grid, including price-based air-conditioning load demand response and interruptible air-conditioning load that can be scheduled on the day before, emergency air-conditioning load demand response for intraday scheduling, and direct air-conditioning load demand response for real-time scheduling.

[0068] The time scale of the day-ahead dispatch plan is 1 hour, and the execution cycle is 24 hours. In the day-ahead dispatch, it is necessary to determine the price-based (PDR) air conditioning load response and the interruptible load (IL) dispatch plan, and execute the intraday plan as the dispatch scheme; the time scale of the intraday rolling optimization is 15 minutes, and the execution cycle is 4 hours. In the intraday dispatch, the PDR and IL dispatch plans of the day-ahead stage are adopted, and the charging and discharging power of the energy storage power station and the emergency demand response (EDR) dispatch plan are formulated, and the above dispatch plans are adopted in the real-time dispatch plan; the real-time optimization strategy execution cycle is 5 minutes, and the direct load control (DLC) dispatch plan is formulated with reference to the intraday rolling curve, and the actual working conditions are corrected in real time to ensure the smooth operation of the distribution network.

[0069] The demand response dispatch cost and proportion information of each type of air conditioning load are given in Table 1 below.

[0070] Table 1 Fault line parameters

[0071]

[0072] Step 3: Based on the distribution network information in step 1, considering the distribution network flow constraints, energy storage system operation constraints, distributed power generation operation constraints and load loss constraints, determine the constraints for the operation of the distribution network;

[0073] a) Distribution network flow constraints:

[0074]

[0075]

[0076]

[0077]

[0078] in, represents the set of parent nodes of node k, represents the set of parent nodes of node j, and Respectively represent the active power and reactive power on branch ij at time t, and Respectively represent the active power and reactive power on branch jk at time t, and They are the square of the current value of branch ij and the square of the voltage value of node i at time t, is the node voltage of node j at time t, and is the minimum and maximum node voltage of node j at time t, and is the voltage and reactance value of branch ij, and is the active load and reactive load of node j at time t, is the output of the energy storage system at node j at time t, is the number of energy storage systems, and is the active power and reactive power of the distributed generation at node j at time t, is the number of distributed energy resources, and is the active and reactive call amount of the air conditioning load of node j at time t when executing IL, and is the active and reactive call amount when the air conditioning load of node j executes EDR at time t, and is the active load loss and reactive load loss of node j at time t, is the maximum line transmission capacity of branch ij.

[0079] b) Energy storage system operation constraints:

[0080]

[0081] in, is the power of the i-th energy storage system at time t, is the power of the i-th energy storage system at time t-1, and are the lower and upper limits of the energy storage system respectively. is the charging and discharging power of the energy storage system at time t, is the upper limit of the charging and discharging power of the energy storage system, and are the charging decision variables and discharging decision variables of the energy storage system at node i at time t, and is the charge and discharge efficiency.

[0082] c) Distributed power operation constraints:

[0083]

[0084]

[0085] in, and are the active and reactive outputs of the ith distributed generation at time t, is the maximum active output of the distributed generation predicted at time t, is the capacity of distributed power generation.

[0086] d) Load loss restraint:

[0087]

[0088] Among them, the load loss constraint Indicates that the load loss of node i at time t cannot exceed the node prediction value .

[0089] Step 4: Based on the distribution network line information obtained in step 1, considering the operation characteristics of air-conditioning loads under multiple time scales, differentiated constraint conditions for air-conditioning load demand responses under different time scales are constructed;

[0090] The time scale is divided into daily, hourly and minute-level dispatching scales to adapt to demand response signals of different frequencies, corresponding to the day-ahead, intraday and real-time optimization dispatching scales respectively;

[0091] 1) Day-ahead scheduling constraints:

[0092]

[0093] 2) Intraday scheduling constraints:

[0094]

[0095] 3) Real-time scheduling constraints:

[0096]

[0097] in, is the capacity of the air conditioning load at node i at time t that complies with the price-based air conditioning load demand response, and are the minimum and maximum values ​​of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the interruptible air conditioning load, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the emergency air conditioning load demand response, is the maximum value of the air conditioning load that can be urgently adjusted by the air conditioning load aggregator. is the response capacity of the air conditioning load of node i at time t to the direct air conditioning load control demand, It is the maximum value of the air conditioning load that can be directly controlled by the air conditioning load aggregator.

[0098] Step 5: Based on the time scale response characteristics of air conditioning loads at different times in step 4, and considering the user experience and economic characteristics of the distribution network, a multi-level objective function considering the multi-time scale air conditioning load demand response is constructed, as follows:

[0099] 1) Objective function of day-ahead scheduling:

[0100]

[0101]

[0102] 2) Objective function of intraday scheduling:

[0103]

[0104] 3) Objective function of real-time scheduling:

[0105]

[0106]

[0107] in, , , are the objective functions for day-ahead, intra-day, and real-time scheduling, respectively. , , Represent the cost functions of energy storage system, user load and distributed energy respectively, T represents the execution cycle time under different time scales, is the number of energy storage systems, is the energy storage system cost function, is the energy storage system maintenance cost function, Represents the depreciation cost of the energy storage system per unit charge and discharge times, and are 0-1 variables representing the charging and discharging states of the energy storage power station, N is the number of nodes containing air conditioning loads, The cost coefficient for performing IL for the air conditioning load, Cost factor for performing EDR for air conditioning load, is the cost coefficient of the user's lost load, is the call volume of the air conditioning load of node i when executing IL at time t, is the call volume when the air conditioning load of node i executes EDR at time t, is the load loss of the user at node i at time t, is the number of distributed energy resources, is the i-th distributed energy cost function at time t, is the penalty cost coefficient for abandoned light, To predict the distributed energy processing value at time t, is the output value at time t under the actual scheduling strategy;

[0108] The objective function of the intraday rolling optimization is similar to that of the day-ahead dispatch model. However, in the intraday rolling optimization stage, the PDR call volume of the air conditioning load and the demand response capacity of the IL are known, and the total load cost is changed to the sum of the EDR and DLC demand response capacities. and There is no change compared with the day-ahead dispatch model, except that the constraints on the DLC demand response capacity are added, where: is the response capacity of the air conditioning load of node i at time t to meet the IL demand, The maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator Cost factor for performing DLC ​​for air conditioning load, is the response capacity of the air conditioning load of node i at time t to meet the EDR demand, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the call amount when the air conditioning load of node i executes DLC at time t, is the active power call amount when the air conditioning load of node i executes IL at time t, is the active power call amount when the air-conditioning load at node i executes EDR at time t; in the real-time scheduling stage, the PDR response capacity, IL response capacity, and EDR response capacity of the air-conditioning load are all known, and the output behavior pattern of the energy storage system is obtained in the day-ahead optimization stage. Therefore, the related cost of the energy storage system is a known quantity and will not be used as part of the objective function to simplify the model.

[0109] Step 6. According to the air-conditioning load demand response constraints and objective functions at different time scales in step 4 and step 5, combined with the distribution network line information, node user air-conditioning load information, and distribution network operation constraints, use a solver (such as gurobi, cplex, etc.) to solve the distribution network dispatching strategy considering multi-time scale air-conditioning load demand response, and obtain the demand response dispatching strategy of generalized controllable resources under different objective functions at multiple time scales such as day-ahead, intraday, and real-time, including price-based air-conditioning load demand response dispatching strategy, interruptible air-conditioning load dispatching strategy, energy storage system charging and discharging strategy, emergency air-conditioning load demand response dispatching strategy, and direct air-conditioning load control demand response dispatching strategy.

[0110] In this embodiment, the output of each part of the 33 nodes of the distribution network participating in the coordinated regulation is as follows: Figure 3 As shown in the figure, at night, photovoltaic power does not work, the overall load level is high, and the grid operation pressure is high. Therefore, the air conditioning load receives price-based and incentive-based scheduling, tends to reduce the use of load, and energy storage output supports grid operation. During the photovoltaic power generation stage, energy storage is charged to absorb photovoltaic energy, and the air conditioning load is transferred to the current stage. The call status of various DR resources in the distribution network is shown in the figure. Figure 4 As shown. During the day, PDR is mainly used to absorb photovoltaic power. At night, due to rising prices, air conditioning load usage decreases, effectively reducing the load peak. IDR resources are similar to PDR in the day-ahead phase, while intraday and real-time DR resources are mainly used to respond to real-time load conditions, including the correction of the day-ahead plan and its coordination with resources such as energy storage.

[0111] Comparative Example

[0112] In order to verify the effectiveness of the method proposed in the present invention, a comparative example is given, in which: Method 1. The energy storage power station in the distribution network is removed, and only the air conditioning load participates in the coordinated operation of the distribution network; Method 2. The air conditioning load aggregator is not considered to participate in the coordinated operation of the distribution network, that is, the traditional source-grid-storage coordinated operation mode. The results of the comparative scheduling method are shown in Table 2 below:

[0113] Table 2 Comparison of cost and average load loss rate

[0114]

[0115] It can be seen from the table that method 1 is a solution without energy storage. Due to the high load loss, it causes serious economic losses and excessively high dispatching costs. Method 2 adds energy storage, which can greatly reduce the load loss of the distribution system. However, compared with the air-conditioning load aggregator participating in the dispatching solution of the method of the present invention, the load loss is greater and the dispatching cost is also higher. By analogy to the actual distribution network, the economic losses caused will be even greater.

[0116] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the distribution network scheduling method considering multi-time scale air conditioning load demand response in Example 1, and the processor is configured to execute the program stored in the memory.

[0117] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the distribution network scheduling method considering multi-time-scale air conditioning load demand response in embodiment 1.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network dispatching method considering multi-time scale air conditioning load demand response, characterized in that: The following steps are involved: Step 1: Traverse the entire distribution network and count the number of nodes K and the distance between nodes d in the network ij and branch unit impedance, and number the nodes; Step 2: Screen and distribute the air conditioning load conditions of users at each node in the power grid, including price-based air conditioning load demand response and interruptible air conditioning load that can be scheduled on the day before, emergency air conditioning load demand response for intraday scheduling, and direct air conditioning load demand response for real-time scheduling; Step 3: Consider the power flow constraints of the distribution network, the operation constraints of the energy storage system, the operation constraints of the distributed generation system, and the load loss constraints to determine the operation constraints of the distribution network; Step 4: Based on the distribution network line information and considering the operation characteristics of air-conditioning loads at multiple time scales, differentiated constraints for air-conditioning load demand responses at different time scales are constructed; Step 5: Based on the time scale response characteristics of air conditioning loads at different times, and considering the user experience and economic characteristics of the distribution network, a multi-level objective function considering the multi-time scale air conditioning load demand response is constructed. The specific method is as follows: 1) Objective function of day-ahead scheduling: 2) Objective function of intraday scheduling: 3) Objective function of real-time scheduling: in, , , are the objective functions for day-ahead, intra-day, and real-time scheduling, respectively. , , Represent the cost functions of energy storage system, user load and distributed energy respectively, T represents the execution cycle time under different time scales, is the number of energy storage systems, is the energy storage system cost function, is the energy storage system maintenance cost function, Represents the depreciation cost of the energy storage system per unit charge and discharge times, and are 0-1 variables representing the charging and discharging states of the energy storage power station, N is the number of nodes containing air conditioning loads, The cost coefficient for performing IL for the air conditioning load, Cost factor for performing EDR for air conditioning load, is the cost coefficient of the user's lost load, is the call volume of the air conditioning load of node i when executing IL at time t, is the call volume when the air conditioning load of node i executes EDR at time t, is the load loss of the user at node i at time t, is the number of distributed energy resources, is the i-th distributed energy cost function at time t, is the penalty cost coefficient for abandoned light, To predict the distributed energy processing value at time t, is the output value at time t under the actual scheduling strategy; The objective function of the intraday rolling optimization is similar to that of the day-ahead dispatch model. However, in the intraday rolling optimization stage, the PDR call volume of the air conditioning load and the demand response capacity of the IL are known, and the total load cost is changed to the sum of the EDR and DLC demand response capacities. and There is no change compared with the day-ahead dispatch model, except that the constraints on the DLC demand response capacity are added, where: is the response capacity of the air conditioning load of node i at time t to meet the IL demand, The maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator Cost factor for performing DLC ​​for air conditioning load, is the response capacity of the air conditioning load of node i at time t to meet the EDR demand, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the call amount when the air conditioning load of node i executes DLC at time t, is the active power call amount when the air conditioning load of node i executes IL at time t, is the active power call amount when the air conditioning load of node i executes EDR at time t; in the real-time scheduling stage, the PDR response capacity, IL response capacity, and EDR response capacity of the air conditioning load are all known, and the output behavior pattern of the energy storage system is obtained in the day-ahead optimization stage, so the related cost of the energy storage system is a known quantity and will not be used as part of the objective function to simplify the model; Step 6: According to the distribution network line information, the multi-time scale air conditioning load operation characteristics and the generalized controllable resource operation characteristics, the distribution network operation constraints and the multi-time scale air conditioning load operation constraints are considered to obtain the generalized controllable resource scheduling and operation plan at multiple stages and different time scales.

2. The distribution network dispatching method considering multi-time scale air conditioning load demand response according to claim 1 is characterized in that: The formula for the distribution network flow constraint described in step 3 is as follows: in, represents the set of parent nodes of node k, represents the set of parent nodes of node j, and Respectively represent the active power and reactive power on branch ij at time t, and Respectively represent the active power and reactive power on branch jk at time t, and They are the square of the current value of branch ij and the square of the voltage value of node i at time t, is the node voltage of node j at time t, and is the minimum and maximum node voltage of node j at time t, and is the voltage and reactance value of branch ij, and is the active load and reactive load of node j at time t, is the output of the energy storage system at node j at time t, is the number of energy storage systems, and is the active power and reactive power of the distributed generation at node j at time t, is the number of distributed energy resources, and is the active and reactive call amount of the air conditioning load of node j at time t when executing IL, and is the active and reactive call amount when the air conditioning load of node j executes EDR at time t, and is the active load loss and reactive load loss of node j at time t, is the maximum line transmission capacity of branch ij.

3. The distribution network dispatching method considering multi-time scale air conditioning load demand response according to claim 2 is characterized in that: The formula for the energy storage system operation constraints described in step 3 is as follows: in, is the power of the i-th energy storage system at time t, is the power of the i-th energy storage system at time t-1, and are the lower and upper limits of the energy storage system respectively. is the charging and discharging power of the energy storage system at time t, is the upper limit of the charging and discharging power of the energy storage system, and are the charging decision variables and discharging decision variables of the energy storage system at node i at time t, and is the charge and discharge efficiency.

4. The distribution network dispatching method considering multi-time scale air conditioning load demand response according to claim 3 is characterized in that: The formula for the distributed generation operation constraints described in step 3 is as follows: in, and are the active and reactive outputs of the ith distributed generation at time t, is the maximum active output of the distributed generation predicted at time t, is the capacity of distributed power generation.

5. The distribution network dispatching method considering multi-time scale air conditioning load demand response according to claim 4 is characterized in that: The formula for the load loss constraint described in step 3 is as follows: Among them, the load loss constraint Indicates that the load loss of node i at time t cannot exceed the node prediction value .

6. The distribution network dispatching method considering multi-time scale air conditioning load demand response according to claim 5, characterized in that: The method described in step 4 to construct differentiated constraints for air conditioning load demand response at different time scales based on distribution network line information and considering the operating characteristics of air conditioning load at multiple time scales is as follows: The time scale is divided into daily, hourly and minute-level dispatching scales to adapt to demand response signals of different frequencies, corresponding to the day-ahead, intraday and real-time optimization dispatching scales respectively; 1) Day-ahead scheduling constraints: 2) Intraday scheduling constraints: 3) Real-time scheduling constraints: in, is the capacity of the air conditioning load at node i at time t that complies with the price-based air conditioning load demand response, and are the minimum and maximum values ​​of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the interruptible air conditioning load, is the maximum value of the air conditioning load that can be controlled by the air conditioning load aggregator, is the response capacity of the air conditioning load of node i at time t to meet the emergency air conditioning load demand response, is the maximum value of the air conditioning load that can be urgently adjusted by the air conditioning load aggregator. is the response capacity of the air conditioning load of node i at time t to the direct air conditioning load control demand, It is the maximum value of the air conditioning load that can be directly controlled by the air conditioning load aggregator.

7. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the distribution network scheduling method considering multi-time scale air conditioning load demand response as described in any one of claims 1 to 6, and the processor is configured to execute the program stored in the memory.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network scheduling method considering multi-time-scale air-conditioning load demand response according to any one of claims 1 to 6 are executed.

Images