Distributed power distribution network low-carbon demand response method considering intraday flexible load

Abstract

The invention discloses a distributed power distribution network low-carbon demand response method considering intraday flexible load. The method comprises the following steps: 1, establishing an uncertainty model of total carbon emission in a power system; 2, establishing a demand side response compensation model; 3, establishing a total model consisting of an uncertainty model of total carbon emission in the power system and a demand response compensation model with intraday flexible load; and 4, performing distributed solution on the total model by adopting an alternating direction multiplier method to obtain an intra-day demand response result. According to the method, the load with intra-day flexibility can be stimulated through compensation measures to change the power consumption behavior of the load, a distributed low-carbon demand response mechanism is implemented to optimize the power load plan, peak load shifting can be achieved, and therefore the total carbon emission of a power system can be remarkably reduced.

Description

Technical Field

[0001] The present invention belongs to the field of distributed energy low-carbon demand response, and specifically is a distributed distribution network low-carbon demand response method taking into account intra-day flexible loads. Background Art

[0002] In traditional power systems, electricity supply is mainly provided by fossil fuel power plants, which causes a lot of carbon emissions and environmental pollution. With the development of renewable energy, especially the accelerated development of distributed generation in distribution networks, new challenges have been brought to the safe and economic operation of power systems. Although renewable energy generation does not produce carbon emissions, the power generation of renewable energy is intermittent and uncertain, depending on fluctuating meteorological conditions such as wind speed and solar radiation intensity. At the same time, there is a spatial and temporal mismatch between renewable energy generation and actual electricity demand. Therefore, demand response has become an important strategy to shift electricity demand and promote the use of low-carbon renewable energy generation.

[0003] In recent years, people have been conducting more in-depth research on demand response. In terms of low-carbon demand response models: Qian Liang et al. proposed a low-carbon optimal scheduling method that considers parameter adaptive demand response incentives; Zhou Bowen et al. proposed a new multi-objective optimization model for integrated energy systems, considering carbon emission trading mechanisms and refined load demand response strategies; Duan Jiandong et al. divided multi-energy loads into vertical demand response and horizontal demand response, and constructed a low-carbon economic optimization model for integrated energy systems that integrates power gas refining models and demand response; Cui Yang et al. constructed a generalized comprehensive demand response model based on time-sharing loads, interruptible loads, and alternative loads to achieve low-carbon economic scheduling of microgrids. All of the above low-carbon demand response-related models have reduced the operating costs and carbon dioxide emissions of power systems to a certain extent.

[0004] Although the low-carbon demand response model can be solved by collecting information from all flexible loads, the centralized solution process faces several challenges: in practice, the mechanism of demand response is naturally distributed. After the operator announces the demand response invitation to all potential participants, only a small number of flexible loads will participate in the response and send back the changed load plan as a response. The accurate parameters of all flexible loads on the demand side are difficult to obtain and maintain. The centralized demand response model cannot be solved by partial observability on the demand side. The number of flexible loads is huge, so the computational efficiency of the centralized model decreases when the size of variables and constraints increases. Summary of the invention

[0005] The present invention aims to address the deficiencies of the above-mentioned prior art and proposes a low-carbon demand response method for a distributed distribution network taking into account intraday flexible loads, in order to encourage loads with intraday flexibility to change their electricity consumption behavior through compensation measures, implement a distributed low-carbon demand response mechanism to optimize power load planning, and achieve peak shaving and valley filling, thereby significantly reducing the total carbon emissions of the power system and being more conducive to obtaining the results of demand-side response of intraday flexible loads.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0007] The low-carbon demand response method of a distributed distribution network considering intraday flexible loads of the present invention is characterized in that it comprises the following steps:

[0008] Step 1: Take the total carbon emissions of the power system at time t As the goal, the uncertainty model of total carbon emissions is established using equations (1) to (3):

[0009] (1)

[0010] (2)

[0011] (3)

[0012] In formula (1) to formula (3), is the number of generators, is the active power generated by the i-th generator at time t, The output power of the i-th generator is The linear carbon emission function is is the carbon emission coefficient of the i-th generator, is the type of distributed power source, is the type set of distributed power sources, for Carbon emission coefficient of type distributed power generation, for Type of active power generated by distributed generation, is the average carbon emission coefficient of the transmission grid power;

[0013] Step 2: Take the demand response compensation value with intraday flexible load at node a at time t as As the goal, the objective function of the demand response compensation model is established using formula (4):

[0014] (4)

[0015] In formula (4), - Represents 5 interval endpoint values, and has < < < < , represents the active power of the intraday flexible load on node a at time t, represents the benchmark active power of the intraday flexible load on node a at time t, , , , is the compensation coefficient corresponding to 4 different intervals;

[0016] A series of equivalent linear constraints of the demand response compensation model are constructed using equations (5) to (8):

[0017] (5)

[0018] (6)

[0019] (7)

[0020] (8)

[0021] Step 3: construct a final model consisting of an uncertainty model of the total carbon emissions of the power system and a demand response compensation model with intraday flexible loads;

[0022] Step 3.1: According to formula (9), a demand response compensation model for total carbon emissions in the power system and intraday flexible load is established on the day t s The objective function J after time:

[0023] (9)

[0024] In formula (9), T represents the upper limit of the intraday flexible load adjustment time, and ID represents the set with intraday flexible loads;

[0025] Step 3.2, establish the total carbon emissions of the power system and the overall constraints of the demand response compensation model with intraday flexible load;

[0026] Step 4: Use the alternating direction multiplier method to perform distributed solution on the total carbon emissions of the power system and the demand response compensation model with intraday flexible load to obtain the intraday demand response results.

[0027] The low-carbon demand response method of a distributed distribution network considering intraday flexible loads according to the present invention is also characterized in that step 3.2 includes the following steps:

[0028] Step 3.2.1: Determine the output power constraints of the generator and distributed generation using equations (10) and (11):

[0029] (10)

[0030] (11)

[0031] In formula (10)-formula (11), , represents the lower and upper limits of the active power generated by the i-th generating equipment at time t, , represents the lower and upper limits of the reactive power generated by the i-th generating equipment at time t, represents the reactive power generated by the i-th generating equipment at time t;

[0032] Step 3.2.2: Determine the power flow constraint of the radial distribution network using equations (12) to (14):

[0033] (12)

[0034] (13)

[0035] (14)

[0036] In formula (12) to formula (14), is the active power flowing from the branch connected to nodes a and b into node b at time t, is the set of power generation equipment connected to node b, m is any node connected to node b except node a, is the active power flowing out of node b from the branch connected to node b and node m at time t, is the set of loads on node b, is the active power of the load on node b at time t, is the reactive power flowing from the branch connected to nodes a and b into node b at time t, is the reactive power flowing out of node b from the branch connected to node b and node m at time t, is the reactive power of the load on node b at time t, , They represent the square of the voltage at node a and node b at time t, , Respectively represent the resistance and reactance of the branch connected to node a and node b;

[0037] Step 3.2.3: Determine the branch capacity constraint using equation (15):

[0038] (15)

[0039] In formula (15), Indicates the maximum capacity allowed on the branch connected to nodes a and b;

[0040] Step 3.2.4: Use equations (16) and (17) to construct node voltage constraints:

[0041] (16)

[0042] (17)

[0043] In formula (16)-formula (17), , They represent the lower and upper limits of the voltage on node a respectively, represents the voltage on node a at time t, It represents the voltage of the node numbered bus-0 on the bus connected to the transmission network at time t. Indicates the reference voltage;

[0044] Step 3.2.5: Use equations (18) to (21) to establish load constraints with intraday flexibility:

[0045] (18)

[0046] (19)

[0047] (20)

[0048] (twenty one)

[0049] In formula (18) to formula (21), is the response delay time of the load on node a, , They represent the lower and upper limits of the active power of the intraday flexible load on node a at time t, Represents the time interval between adjacent moments. , They are the intraday flexible loads on node a running to The lower and upper bounds of energy consumption at all times, represents the power factor angle of the ath node, Indicates the adjustment time when the flexible load starts within the day.

[0050] Further, the step 4 comprises the following steps:

[0051] Step 4.1: Define the decision variables on the grid side at time t and the decision variables on the load side at time t ,in, They represent the active power and reactive power of the intraday flexible load on the grid side node a at time t, They represent the active power and reactive power of the intraday flexible load on the load side node a at time t respectively;

[0052] Step 4.2: Use equations (22) to (26) to construct the transformed constraint conditions:

[0053] (twenty two)

[0054] (twenty three)

[0055] (twenty four)

[0056] (25)

[0057] (26)

[0058] Step 4.3, define the current number of iterations as , and initialize = 0, and initialize the The active power of the flexible load on the load side node a at time t in the iteration and reactive power , initialize the The active power of the flexible load on the grid side node a at time t in the iteration and reactive power , initialize the The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier ;

[0059] Step 4.4: Use formula (27) to construct The objective function at the iteration , and under the transformed constraints, The objective function at the iteration Solve and get The active power of the load on node a on the grid side at time t in the iteration and reactive power :

[0060] (27)

[0061] In formula (27), is the weight factor;

[0062] Step 4.5: , and After substituting into formula (27), Solve and get The active power of the flexible load on the load side node a at time t in the iteration and reactive power ;

[0063] Step 4.6: According to equation (28)-(29), Update to get The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier ;

[0064] (28)

[0065] (29)

[0066] Step 4.7: If and If the maximum value between the two is less than the set convergence criterion, it means that the intraday demand response result is and Otherwise, Assign to Then, return to step 4.4 and execute sequentially.

[0067] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the distributed distribution network low-carbon demand response method, and the processor is configured to execute the program stored in the memory.

[0068] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which is characterized in that when the computer program is executed by a processor, the steps of the distributed distribution network low-carbon demand response method are executed.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. Since loads with intraday flexibility can adjust their power demand with shorter response delays, the present invention further promotes the adaptability of photovoltaic power generation by introducing intraday demand response to use flexible loads with faster response capabilities to handle the deviation between the actual photovoltaic power generation and the photovoltaic power generation predicted the previous day. By optimizing the power generation mix in real time, the intraday adjustment strategy helps reduce the use of fossil fuels and reduces greenhouse gas emissions.

[0071] 2. This invention proposes a distributed demand response mechanism based on the alternating direction multiplier method, which solves the defects of low solution efficiency and difficult solution process in centralized solution. Distributed algorithm is more suitable for demand response than centralized optimization scheduling method, which can simplify calculation. Users and operators iteratively exchange their plans to obtain the best demand plan. The hot start intraday adjustment strategy uses the power consumption plan of the day before to accelerate convergence and achieve efficient rescheduling when the photovoltaic power generation fluctuates. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0073] In the low-carbon demand response model of the present invention, there are two optimization objectives: carbon emissions in the power system and compensation for loads with intraday flexibility that participate in demand response. The rapid response capability of loads with intraday flexibility also means that their compensation rate is higher than that of loads with day-ahead flexibility. In this embodiment, a distributed distribution network low-carbon demand response method considering intraday flexible loads is provided. Figure 1 As shown, the following steps are included:

[0074] Step 1: The total carbon emissions in period t are divided into two parts: the carbon emissions generated by local generators and the carbon emissions generated on the transmission network. Considering the inevitable power exchange between the distribution network and the transmission network, the transmission network is regarded as a single power source, and the total carbon emissions of the power system at time t are As the goal, the uncertainty model of total carbon emissions is established using equations (1) to (3):

[0075] (1)

[0076] (2)

[0077] (3)

[0078] In formula (1) to formula (3), is the number of generators, is the active power generated by the i-th generator at time t, The output power of the i-th generator is The linear carbon emission function is is the carbon emission coefficient of the i-th generator, is the type of distributed power source, is the type set of distributed power sources, for Carbon emission coefficient of type distributed power generation, for Type of active power generated by distributed generation, is the average carbon emission coefficient of the transmission grid power, which is the weighted average of different types of power sources.

[0079] Step 2: The power demand with intraday flexible load is adjustable, and the compensation value of demand response depends on the actual load Compared with its baseline power load In the present invention, a piecewise function compensation method is adopted, and the compensation coefficient depends on The value of is the demand response compensation value of the intraday flexible load at node a at time t. As the goal, the objective function of the demand response compensation model is established using formula (4):

[0080] (4)

[0081] In formula (4), - Represents 5 interval endpoint values, and has < < < < , represents the active power of the intraday flexible load on node a at time t, represents the benchmark active power of the intraday flexible load on node a at time t, , , , is the compensation coefficient corresponding to 4 different intervals.

[0082] Because the compensation factor will change with the actual load Compared with its baseline power load The demand response compensation value increases with the increase of the deviation between Relative to actual load is a convex function, which enables us to rewrite the piecewise function into a series of equivalent linear constraints and construct a series of equivalent linear constraints for the demand response compensation model using equations (5) to (8):

[0083] (5)

[0084] (6)

[0085] (7)

[0086] (8)

[0087] Step 3: Construct a final model consisting of an uncertainty model of the total carbon emissions of the power system and a demand response compensation model with intraday flexible loads.

[0088] Step 3.1: According to formula (9), a demand response compensation model for total carbon emissions in the power system and intraday flexible load is established on the day t s The objective function J after time:

[0089] (9)

[0090] In formula (9), T represents the upper limit of the intra-day flexible load adjustment time, and ID represents the set of intra-day flexible loads.

[0091] Step 3.2: Establish the total carbon emissions of the power system and the overall constraints of the demand response compensation model with intraday flexible load, including:

[0092] Step 3.2.1: Determine the output power constraints of the generator and distributed generation using equations (10) and (11):

[0093] (10)

[0094] (11)

[0095] In formula (10)-formula (11), , represents the lower and upper limits of the active power generated by the i-th generating equipment at time t, , represents the lower and upper limits of the reactive power generated by the i-th generating equipment at time t, represents the reactive power generated by the i-th generating equipment at time t;

[0096] Step 3.2.2: Use the linear branch power flow model to describe the power flow constraints in the radial distribution network, and use equations (12) to (14) to determine the power flow constraints of the radial distribution network:

[0097] (12)

[0098] (13)

[0099] (14)

[0100] In formula (12) to formula (14), is the active power flowing from the branch connected to nodes a and b into node b at time t, is the set of power generation equipment connected to node b, m is any node connected to node b except node a, is the active power flowing out of node b from the branch connected to node b and node m at time t, is the set of loads on node b, is the active power of the load on node b at time t, is the reactive power flowing from the branch connected to nodes a and b into node b at time t, is the reactive power flowing out of node b from the branch connected to node b and node m at time t, is the reactive power of the load on node b at time t, , They represent the square of the voltage at node a and node b at time t, , They represent the resistance and reactance of the branches connected to nodes a and b respectively.

[0101] Step 3.2.3: Determine the branch capacity constraint using equation (15):

[0102] (15)

[0103] Formula (15) is a quadratic cone constraint, which is used to limit the power flow below the branch capacity. Indicates the maximum capacity allowed on the branch connected to nodes a and b;

[0104] Step 3.2.4: Use equation (16)-(17) to constrain voltage:

[0105] (16)

[0106] (17)

[0107] In formula (16)-formula (17), , They represent the lower and upper limits of the voltage on node a respectively, represents the voltage on node a at time t, It represents the voltage of the node numbered bus-0 on the bus connected to the transmission network at time t. Indicates the reference voltage.

[0108] Step 3.2.5: The adjustment of the power load with intraday flexibility in demand response is limited by internal physical constraints. This method considers the upper and lower bounds of the power and energy consumed by the adjustable load through the virtual battery model, and uses equations (18) to (21) to establish load constraints with intraday flexibility:

[0109] (18)

[0110] (19)

[0111] (20)

[0112] (twenty one)

[0113] In formula (18) to formula (21), is the response delay time of the load on node a, , They represent the lower and upper limits of the active power of the intraday flexible load on node a at time t, Represents the time interval between adjacent moments. , They are the intraday flexible loads on node a running to The lower and upper bounds of energy consumption at all times, represents the power factor angle of the ath node, Indicates the adjustment time when the flexible load starts within the day.

[0114] Step 4: Since the alternating direction multiplier method is widely used to solve optimization problems under constraints and has achieved satisfactory results, the present invention uses the alternating direction multiplier method to perform distributed solutions to the total carbon emissions of the power system and the demand response compensation model with intraday flexible loads to obtain the intraday demand response results.

[0115] Step 4.1: In order to apply the alternating direction multiplier method to the demand response model, two power load variables are introduced: Auxiliary variables: (1) Power load on the grid side ; (2) Load side power load: Define the decision variables on the grid side at time t and the decision variables on the load side at time t ,in, They represent the active power and reactive power of the intraday flexible load on the grid side node a at time t, They respectively represent the active power and reactive power of the intraday flexible load on node a on the load side at time t.

[0116] Step 4.2: Use the alternating direction multiplier method to rewrite the low-carbon demand response model into a consensus optimization problem. In order to obtain the final solution of the optimal demand response, the power load in the public constraints on the grid side and the local constraints on the load side should be equal. Use equations (22) to (26) to construct the transformed constraints:

[0117] (twenty two)

[0118] (twenty three)

[0119] (twenty four)

[0120] (25)

[0121] (26)

[0122] Step 4.3, define the current number of iterations as , and initialize = 0, and initialize the The active power of the flexible load on the load side node a at time t in the iteration and reactive power , initialize the The active power of the flexible load on the grid side node a at time t in the iteration and reactive power , initialize the The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier .

[0123] Step 4.4: Use formula (27) to construct The objective function at the iteration , and under the transformed constraints, The objective function at the iteration Solve and get The active power of the load on node a on the grid side at time t in the iteration and reactive power :

[0124] (27)

[0125] In formula (27), is the weight factor.

[0126] Step 4.5: , and After substituting into formula (27), Solve and get The active power of the flexible load on the load side node a at time t in the iteration and reactive power ;

[0127] Step 4.6: According to equation (28)-(29), Update to get The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier ;

[0128] (28)

[0129] (29)

[0130] Step 4.7: If and If the maximum value between the two is less than the set convergence criterion, it means that the intraday demand response result is and , all participants in the demand response during the day will respond to the delay Then adjust its load, otherwise, Assign to Then, return to step 4.4 and execute sequentially.

[0131] In this embodiment, 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 method, and the processor is configured to execute the program stored in the memory.

[0132] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A low-carbon demand response method for a distributed distribution network considering intraday flexible loads, characterized in that: The following steps are involved: Step 1: Take the total carbon emissions of the power system at time t As the goal, the uncertainty model of total carbon emissions is established using equations (1) to (3): (1) (2) (3) In formula (1) to formula (3), is the number of generators, is the active power generated by the i-th generator at time t, The output power of the i-th generator is The linear carbon emission function is is the carbon emission coefficient of the i-th generator, is the type of distributed power source, is the type set of distributed power sources, for Carbon emission coefficient of type distributed power generation, for Type of active power generated by distributed generation, is the average carbon emission coefficient of the transmission grid power; Step 2: Take the demand response compensation value with intraday flexible load at node a at time t as As the goal, the objective function of the demand response compensation model is established using formula (4): (4) In formula (4), - Represents 5 interval endpoint values, and has < < < < , represents the active power of the intraday flexible load on node a at time t, represents the benchmark active power of the intraday flexible load on node a at time t, , , , is the compensation coefficient corresponding to 4 different intervals; A series of equivalent linear constraints of the demand response compensation model are constructed using equations (5) to (8): (5) (6) (7) (8) Step 3: construct a final model consisting of an uncertainty model of the total carbon emissions of the power system and a demand response compensation model with intraday flexible loads; Step 3.1: According to formula (9), a demand response compensation model for total carbon emissions in the power system and intraday flexible load is established on the day t s The objective function J after time: (9) In formula (9), T represents the upper limit of the intraday flexible load adjustment time, and ID represents the set with intraday flexible loads; Step 3.2, establish the total carbon emissions of the power system and the overall constraints of the demand response compensation model with intraday flexible load; Step 4: Use the alternating direction multiplier method to perform distributed solution on the total carbon emissions of the power system and the demand response compensation model with intraday flexible load to obtain the intraday demand response results.

2. A distributed distribution network low-carbon demand response method considering intraday flexible load according to claim 1, characterized in that: The step 3.2 comprises the following steps: Step 3.2.1: Determine the output power constraints of the generator and distributed generation using equations (10) and (11): (10) (11) In formula (10)-formula (11), , represents the lower and upper limits of the active power generated by the i-th generating equipment at time t, , represents the lower and upper limits of the reactive power generated by the i-th generating equipment at time t, represents the reactive power generated by the i-th generating equipment at time t; Step 3.2.2: Determine the power flow constraint of the radial distribution network using equations (12) to (14): (12) (13) (14) In formula (12) to formula (14), is the active power flowing from the branch connected to nodes a and b into node b at time t, is the set of power generation equipment connected to node b, m is any node connected to node b except node a, is the active power flowing out of node b from the branch connected to node b and node m at time t, is the set of loads on node b, is the active power of the load on node b at time t, is the reactive power flowing into node b from the branch connected to node a and node b at time t, is the reactive power flowing out of node b from the branch connected to node b and node m at time t, is the reactive power of the load on node b at time t, , They represent the square of the voltage at node a and node b at time t, , Respectively represent the resistance and reactance of the branch connected to node a and node b; Step 3.2.3: Determine the branch capacity constraint using equation (15): (15) In formula (15), Indicates the maximum capacity allowed on the branch connected to nodes a and b; Step 3.2.4: Use equations (16) and (17) to construct node voltage constraints: (16) (17) In formula (16)-formula (17), , They represent the lower and upper limits of the voltage on node a respectively, represents the voltage on node a at time t, It represents the voltage of the node numbered bus-0 on the bus connected to the transmission network at time t. Indicates the reference voltage; Step 3.2.5: Use equations (18) to (21) to establish load constraints with intraday flexibility: (18) (19) (20) (21) In formula (18) to formula (21), is the response delay time of the load on node a, , They represent the lower and upper limits of the active power of the intraday flexible load on node a at time t, Represents the time interval between adjacent moments. , They are the intraday flexible loads on node a running to The lower and upper bounds of energy consumption at all times, represents the power factor angle of the ath node, Indicates the adjustment time when the flexible load starts within the day.

3. A distributed distribution network low-carbon demand response method considering intraday flexible load according to claim 2, characterized in that: The step 4 comprises the following steps: Step 4.1: Define the decision variables on the grid side at time t and the decision variables on the load side at time t ,in, They represent the active power and reactive power of the intraday flexible load on the grid side node a at time t, They represent the active power and reactive power of the intraday flexible load on the load side node a at time t respectively; Step 4.2: Use equations (22) to (26) to construct the transformed constraint conditions: (22) (23) (24) (25) (26) Step 4.3, define the current number of iterations as , and initialize = 0, and initialize the The active power of the flexible load on the load side node a at time t in the iteration and reactive power , initialize the The active power of the flexible load on the grid side node a at time t in the iteration and reactive power , initialize the The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier ; Step 4.4: Use formula (27) to construct The objective function at the iteration , and under the transformed constraints, The objective function at the iteration Solve and get The active power of the load on node a on the grid side at time t in the iteration and reactive power : (27) In formula (27), is the weight factor; Step 4.5: , and After substituting into formula (27), Solve and get The active power of the flexible load on the load side node a at time t in the iteration and reactive power ; Step 4.6: According to equation (28)-(29), Update to get The active power multiplier of the intraday flexible load on node a at time t in the iteration and reactive power multiplier ; (28) (29) Step 4.7: If and If the maximum value between the two is less than the set convergence criterion, it means that the intraday demand response result is and Otherwise, Assign to Then, return to step 4.4 and execute sequentially.

4. 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 distributed distribution network low-carbon demand response method as described in any one of claims 1-3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed distribution network low-carbon demand response method according to any one of claims 1 to 3 are executed.

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