Microgrid group multi-time scale optimal scheduling method considering carbon trading and demand side response

Abstract

The application discloses a kind of micro-grid group multi-time scale optimization scheduling method considering carbon trading and demand side response, comprising: 1) according to the time scale of demand side response participation;With the minimum daily loss as objective function, with output constraint as constraint condition, construct micro-grid group day-ahead optimization scheduling model and solve, obtain micro-grid group day-ahead scheduling result and daily carbon quota demand and discard amount;2) according to micro-grid group day-ahead scheduling result and carbon quota demand and discard amount, calculate daily carbon quota pre-allocation quota;3) according to micro-grid group day-ahead scheduling result and daily carbon quota pre-allocation quota of each period, with the minimum daily loss as objective function, with output constraint as constraint condition, construct micro-grid group daily optimization scheduling model and solve, obtain micro-grid group daily scheduling result.The application considers carbon trading and demand side response in micro-grid group multi-time scale optimization scheduling, can utilize load side resource, reduce the loss of micro-grid group operation.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling, specifically a multi-timescale optimization scheduling method for microgrid groups that considers carbon trading and demand-side response. Background Technology

[0002] With the increasing prominence of the adverse effects caused by greenhouse gas emissions, my country has vigorously promoted "dual carbon" initiatives in recent years, encouraging the green and low-carbon transformation of industries. As a crucial link in this ongoing green and low-carbon transformation, the power system faces significant challenges in developing renewable energy and advancing carbon emission reduction. On the one hand, achieving carbon peaking and carbon neutrality is a systemic transformation. To achieve this transformation, a sound market mechanism supporting it is needed. Carbon emission trading can leverage market rules to regulate the market and encourage operators to assume environmental responsibility for their production and consumption activities. On the other hand, the large-scale integration of new energy sources poses a severe challenge to the stable operation of microgrid power systems. Therefore, fully tapping the potential of wind and solar power, as well as demand-side dispatching within microgrids, is essential for ensuring the safe and stable operation of the power grid and the development of new energy sources. This requires that optimized microgrid dispatching, while maintaining low energy consumption, also improves the absorption rate of clean energy in day-ahead dispatching strategies and the participation of demand-side response within the microgrid.

[0003] Current research has yielded some findings on microgrid optimal dispatch. While numerous optimization methods exist, they primarily focus on reducing microgrid losses, lacking research on demand-side response potential and carbon emission trading within microgrids. With the continuous improvement of the electricity market and advancements in grid technology, some of the microgrid optimal dispatch methods proposed in existing studies have been applied in certain regions.

[0004] The above microgrid optimization scheduling methods take into account the microgrid loss value under the microgrid scheduling strategy. However, this microgrid optimization scheduling mainly emphasizes the reduction of microgrid loss value. Users lack the initiative to actively shaving peaks, and the peak shaving pressure of batteries is relatively large. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a multi-timescale optimization scheduling method for microgrid groups that considers carbon trading and demand-side response. The aim is to reduce the energy consumption of microgrids, fully tap the scheduling potential of the microgrid demand side, thereby improving the absorption rate of clean energy in microgrids and ensuring the safe and stable operation of the microgrid power system and the development of new energy sources.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The present invention provides a multi-timescale optimal scheduling method for microgrid groups that considers carbon trading and demand-side response, characterized by the following steps:

[0008] Step 1: Based on the time scale of demand-side response participation; with the minimum day-ahead loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the day-ahead optimization scheduling model of the microgrid group to obtain the day-ahead scheduling results of the microgrid group and the day-ahead carbon quota demand and discard amount.

[0009] Step 2: Calculate the daily carbon quota pre-allocation based on the microgrid group's day-ahead dispatch results, carbon quota demand, and waste amount;

[0010] Step 3: Based on the day-ahead scheduling results of the microgrid group and the pre-allocated carbon quota for each time period during the day, with the minimum intraday loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the intraday optimization scheduling model of the microgrid group to obtain the intraday scheduling results of the microgrid group.

[0011] The microgrid group multi-timescale optimal scheduling method considering carbon trading and demand-side response described in this invention is also characterized in that step 1 includes:

[0012] Step 1.1: Divide the demand-side response of the microgrid load side into Class A demand-side response (ADR) and Class B demand-side response (BDR). The response time of Class A demand-side response (ADR) is on the hourly level, and the response time of Class B demand-side response (BDR) is on the minute level. Class A demand-side response (ADR) participates in the day-ahead dispatch of the microgrid, and Class B demand-side response (BDR) participates in the intraday dispatch of the microgrid.

[0013] Step 1.2: Construct the day-ahead stage objective function with the goal of minimizing day-ahead losses using equation (1). :

[0014] (1)

[0015] In equation (1), f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the carbon trading loss of the k-th microgrid in the microgrid group participating in the day-ahead carbon trading market; This represents the call loss of the k-th microgrid in the microgrid group when calling the ADR (Advanced Demand Response) of type A on the day-ahead; T represents the total day-ahead scheduling period, and K represents the total number of microgrids in the microgrid group;

[0016] Step 1.3: Construct the power balance constraint at time t during the day-ahead phase using equation (2):

[0017] (2)

[0018] In equation (2), P load,t P represents the total load of the microgrid at time t;WT,t P represents the output power of the wind turbine generator set WT at time t; PV,t P represents the output power of the photovoltaic generator set at time t; ADR,t P represents the amount of Type A demand-side response (ADR) participating in scheduling at time t; MT,t This represents the output power of the micro gas turbine MT at time t; It is a 0-1 variable, when A value of 1 indicates that the battery is charging; when A value of 0 indicates that the battery is discharging; P BA,t Let BA be the output power of the battery at time t; For the charging efficiency of the storage battery; P represents the discharge efficiency of the battery. GRID,t Let t be the power interaction between the upper-level grid GRID and the microgrid group at time t;

[0019] Step 1.4: Construct the output constraints of each micro-source at time t during the day-ahead phase using equation (3):

[0020] (3)

[0021] In equation (3), Let represent the minimum output constraint of the i-th micro-source at time t. This represents the maximum output constraint of the i-th micro-power source at time t;

[0022] Step 1.5: Construct the requirement-side response call volume constraint for the current-day phase A using equation (4):

[0023] (4)

[0024] In equation (4), Let t be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let t be the maximum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t.

[0025] f in step 1.2 k,t From equation (5), we get:

[0026] (5)

[0027] In equation (5), CBA k,t, CMT k,t, CPV k,t, CWT k,t, and Cgrid k,t represent the operating losses of the battery BA, micro gas turbine MT, photovoltaic unit PV, wind turbine WT, and the power exchange losses between the microgrid and the upper-level grid GRID in the k-th microgrid group at time t, respectively.

[0028] (6)

[0029] In equation (6), c BA c MT c PV c WT These are the operating loss coefficients for the battery (BA), micro gas turbine (MT), photovoltaic generator (PV), and wind turbine generator (WT), respectively; C gridsell,k,t C is a function of the power transmission from the upper-level power grid to the k-th microgrid at time t; gridbuy,k,t It is a function that enables the k-th microgrid to transmit power to the upper-level grid; It is a 0-1 variable, when When =1, it means P GRID,t <0; when When =0, it means P GRID,t >0;

[0030] In step 1.2 From equation (7), we get:

[0031] (7)

[0032] In equation (7), C is a function of the current-day demand for carbon allowances per unit; average,MT This is the industry benchmark value for CO2 emissions per unit output of a micro gas turbine (MT). Let be the output power of the micro gas turbine MT in the k-th microgrid group at time t; Let be the CO2 emission factor of the micro gas turbine MT in the kth microgrid in the microgrid group;

[0033] In step 1.2 From equation (8), we get:

[0034] (8)

[0035] In equation (8), C k,ADR For the k-th microgrid in the microgrid group, call the function of type A demand-side response (ADR), P k,ADR,tLet be the amount of Type A demand-side response (ADR) invoked by the k-th microgrid at time t.

[0036] Step 2 includes:

[0037] Step 2.1: Based on the day-ahead scheduling results of the microgrid group and the carbon quota demand and discard amount, calculate the carbon quota pre-allocation amount at time t within the day using equation (9). :

[0038] (9)

[0039] In equation (9), t in t represents the time scale for intraday scheduling of microgrid groups. ahead This indicates the time scale for day-ahead scheduling of microgrid groups. This represents the output power of the i-th type of micro-source in the k-th microgrid in the microgrid group at time t.

[0040] Step 3 includes:

[0041] Step 3.1: Construct the intraday objective function with the goal of minimizing the losses of the microgrid group using equation (10). :

[0042] (10)

[0043] In equation (10), The invocation overhead for the Class A demand-side response (ADR) invoked at time t, as determined in the previous phase; f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the intraday carbon trading loss of the k-th microgrid in the microgrid group at time t; This represents the loss of the k-th microgrid in the microgrid group when it invokes the B-type demand-side response (BDR) at time t;

[0044] Step 3.2: Construct the power balance constraint at time t during the intraday phase using equation (11):

[0045] (11)

[0046] In equation (11), P BDR,t This represents the amount of type B demand-side response that participates in scheduling at time t.

[0047] Step 3.3: Construct the output constraint of the i-th micro-power source at time t during the intraday phase using equation (12):

[0048] (12)

[0049] Step 3.4: Construct the intraday A-side demand response ADR call volume constraint using equation (13):

[0050] (13)

[0051] In equation (13), Let be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let be the maximum value of the type A demand-side response (ADR) that can be invoked in the microgrid group at time t;

[0052] Step 3.5: Construct the intraday phase A-type demand-side response (BDR) call volume constraint using equation (14):

[0053] (14)

[0054] In equation (14), Let t be the minimum value of the type B demand-side response (BDR) that can be called in the microgrid group at time t; Let t be the maximum value of the B-type demand-side response (BDR) that can be called in the microgrid group at time t.

[0055] In step 3.1 From equation (15), we get:

[0056] (15)

[0057] In equation (15), The coefficient function for the unit carbon allowance required within the day;

[0058] In step 3.1 From equation (16), we get:

[0059] (16)

[0060] In equation (16), C k,BDR To invoke the coefficient function of the Type B Demand-Side Response (BDR) for the k-th microgrid in the microgrid group, P k,BDR,t Let be the amount of the Type B Demand Response (BDR) invoked by the k-th microgrid in the microgrid group at time t.

[0061] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the microgrid group multi-time-scale optimization scheduling method, and the processor is configured to execute the program stored in the memory.

[0062] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the microgrid group multi-time-scale optimization scheduling method.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] 1. The day-to-day carbon trading model proposed in this invention takes into account the environmental impact of microgrid operation, which is conducive to the clean operation of microgrids under low energy consumption.

[0065] 2. The demand-side response scheduling strategy proposed in this invention under multiple time scales fully taps the scheduling potential of demand-side response in microgrids, improves the enthusiasm of users to actively participate in peak shaving, and reduces the peak shaving pressure on the power grid.

[0066] 3. The method of the present invention takes into account both the environmental impact of microgrid operation and the dispatch potential of the user side in the microgrid, reduces the pollution of the microgrid to the environment, and improves the absorption rate of clean energy in the microgrid. Compared with the unoptimized method, the method of the present invention reduces the environmental pollution of the microgrid, increases the enthusiasm of users to actively participate in peak shaving, reduces the peak shaving pressure on the grid side, and effectively ensures the stable operation of the microgrid power system. Attached Figure Description

[0067] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0068] In this embodiment, to comprehensively consider pollutant treatment and the absorption rate of new energy sources, fully mobilize the enthusiasm of users to participate in microgrid dispatch, and reduce the peak-shaving pressure on the grid side, this invention proposes a multi-time-scale optimal dispatch method for microgrid groups that considers carbon trading and demand-side response. Figure 1 As shown, specifically, it includes the following steps:

[0069] Step 1: Based on the time scale of demand-side response participation; with the minimum day-ahead loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the day-ahead optimization scheduling model of the microgrid group to obtain the day-ahead scheduling results of the microgrid group and the day-ahead carbon quota demand and discard amount.

[0070] Step 1.1: Divide the demand-side response of the microgrid load side into Class A demand-side response (ADR) and Class B demand-side response (BDR). The response time of Class A demand-side response (ADR) is on the hourly level, and the response time of Class B demand-side response (BDR) is on the minute level. Class A demand-side response (ADR) participates in the day-ahead dispatch of the microgrid, and Class B demand-side response (BDR) participates in the intraday dispatch of the microgrid.

[0071] Step 1.2: Construct the day-ahead stage objective function with the goal of minimizing day-ahead losses using equation (1). :

[0072] (1)

[0073] In equation (1), f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the carbon trading loss of the k-th microgrid in the microgrid group participating in the day-ahead carbon trading market; This represents the day-ahead call loss for the k-th microgrid in the microgrid group to invoke a Type A demand-side response (ADR); T represents the total day-ahead scheduling period, and K represents the total number of microgrids in the microgrid group; and we have:

[0074] (5)

[0075] In equation (5), CBA k,t, CMT k,t, CPV k,t, CWT k,t, and Cgrid k,t represent the operating losses of the battery BA, micro gas turbine MT, photovoltaic unit PV, wind turbine WT, and the power exchange losses between the microgrid and the upper-level grid GRID in the k-th microgrid group at time t, respectively.

[0076] (6)

[0077] In equation (6), c BA c MT c PV c WT These are the operating loss coefficients for the battery (BA), micro gas turbine (MT), photovoltaic generator (PV), and wind turbine generator (WT), respectively; C gridsell,k,t C is a function of the power transmission from the upper-level power grid to the k-th microgrid at time t; gridbuy,k,t It is a function that enables the k-th microgrid to transmit power to the upper-level grid; It is a 0-1 variable, when When =1, it means P GRID,t <0; when When =0, it means P GRID,t >0;

[0078] (7)

[0079] In equation (7), C is a function of the current-day demand for carbon allowances per unit; average,MT This is the industry benchmark value for CO2 emissions per unit output of a micro gas turbine (MT). Let be the output power of the micro gas turbine MT in the k-th microgrid group at time t; Let be the CO2 emission factor of the micro gas turbine MT in the kth microgrid in the microgrid group;

[0080] (8)

[0081] In equation (8), C k,ADR For the k-th microgrid in the microgrid group, call the function of type A demand-side response (ADR), P k,ADR,t Let be the amount of Type A demand-side response (ADR) invoked by the k-th microgrid at time t.

[0082] Step 1.3: Construct the power balance constraint at time t during the day-ahead phase using equation (2):

[0083] (2)

[0084] In equation (2), P load,t P represents the total load of the microgrid at time t; WT,t P represents the output power of the wind turbine generator set WT at time t; PV,t P represents the output power of the photovoltaic generator set at time t; ADR,t P represents the amount of Type A demand-side response (ADR) participating in scheduling at time t; MT,t This represents the output power of the micro gas turbine MT at time t; It is a 0-1 variable, when A value of 1 indicates that the battery is charging; when A value of 0 indicates that the battery is discharging; P BA,t Let BA be the output power of the battery at time t; For the charging efficiency of the storage battery; P represents the discharge efficiency of the battery. GRID,t Let t be the power interaction between the upper-level grid GRID and the microgrid group at time t;

[0085] Step 1.4: Construct the output constraints of each micro-source at time t during the day-ahead phase using equation (3):

[0086] (3)

[0087] In equation (3), Let represent the minimum output constraint of the i-th micro-source at time t. This represents the maximum output constraint of the i-th micro-power source at time t;

[0088] Step 1.5: Construct the requirement-side response call volume constraint for the current-day phase A using equation (4):

[0089] (4)

[0090] In equation (4), Let t be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let t be the maximum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t.

[0091] Step 2: Calculate the daily carbon quota pre-allocation based on the microgrid group's day-ahead dispatch results, carbon quota demand, and waste amount;

[0092] Step 2.1: Based on the day-ahead scheduling results of the microgrid group and the carbon quota demand and discard amount, calculate the carbon quota pre-allocation amount at time t within the day using equation (9). :

[0093] (9)

[0094] In equation (9), t in t represents the time scale for intraday scheduling of microgrid groups. ahead This indicates the time scale for day-ahead scheduling of microgrid groups. This represents the output power of the i-th type of micro-source in the k-th microgrid in the microgrid group at time t.

[0095] Step 3: Based on the day-ahead scheduling results of the microgrid group and the pre-allocated carbon quota for each time period during the day, with the minimum intraday loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the intraday optimization scheduling model of the microgrid group to obtain the intraday scheduling results of the microgrid group.

[0096] Step 3.1: Construct the intraday objective function with the goal of minimizing the losses of the microgrid group using equation (10). :

[0097] (10)

[0098] In equation (10), The invocation overhead for the Class A demand-side response (ADR) invoked at time t, as determined in the previous phase; f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the intraday carbon trading loss of the k-th microgrid in the microgrid group at time t; This represents the loss of the k-th microgrid in the microgrid group when it invokes the Type B Demand Response (BDR) at time t; and we have:

[0099] (15)

[0100] In equation (15), The coefficient function for the unit carbon allowance required within the day;

[0101] (16)

[0102] In equation (16), C k,BDR To invoke the coefficient function of the Type B Demand-Side Response (BDR) for the k-th microgrid in the microgrid group, P k,BDR,t Let be the amount of the Type B Demand Response (BDR) invoked by the k-th microgrid in the microgrid group at time t.

[0103] Step 3.2: Construct the power balance constraint at time t during the intraday phase using equation (11):

[0104] (11)

[0105] In equation (11), P BDR,t This represents the amount of type B demand-side response that participates in scheduling at time t.

[0106] Step 3.3: Construct the output constraint of the i-th micro-power source at time t during the intraday phase using equation (12):

[0107] (12)

[0108] Step 3.4: Construct the intraday A-side demand response ADR call volume constraint using equation (13):

[0109] (13)

[0110] In equation (13), Let t be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let be the maximum value of the type A demand-side response (ADR) that can be invoked in the microgrid group at time t;

[0111] Step 3.5: Construct the intraday phase A-type demand-side response (BDR) call volume constraint using equation (14):

[0112] (14)

[0113] In equation (14), Let t be the minimum value of the type B demand-side response (BDR) that can be called in the microgrid group at time t; Let t be the maximum value of the B-type demand-side response (BDR) that can be called in the microgrid group at time t.

[0114] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0115] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A multi-timescale optimal scheduling method for microgrid groups considering carbon trading and demand-side response, characterized in that, Includes the following steps: Step 1: Based on the time scale of demand-side response participation; with the minimum day-ahead loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the day-ahead optimization scheduling model of the microgrid group to obtain the day-ahead scheduling results of the microgrid group and the day-ahead carbon quota demand and discard amount. Step 1.1: Divide the demand-side response of the microgrid load side into Class A demand-side response (ADR) and Class B demand-side response (BDR). The response time of Class A demand-side response (ADR) is on the hourly level, and the response time of Class B demand-side response (BDR) is on the minute level. Class A demand-side response (ADR) participates in the day-ahead dispatch of the microgrid, and Class B demand-side response (BDR) participates in the intraday dispatch of the microgrid. Step 1.2: Construct the day-ahead stage objective function with the goal of minimizing day-ahead losses using equation (1). : (1) In equation (1), f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the carbon trading loss of the k-th microgrid in the microgrid group participating in the day-ahead carbon trading market; This represents the day-ahead call loss for the k-th microgrid in the microgrid group to invoke a Type A demand-side response (ADR); T represents the total day-ahead scheduling period, and K represents the total number of microgrids in the microgrid group; and we have: (5) In equation (5), CBA k,t, CMT k,t, CPV k,t, CWT k,t, and Cgrid k,t represent the operating losses of the battery BA, micro gas turbine MT, photovoltaic unit PV, wind turbine WT, and the power exchange losses between the microgrid and the upper-level grid GRID in the k-th microgrid group at time t, respectively. (6) In equation (6), c BA c MT c PV c WT These are the operating loss coefficients for the battery (BA), micro gas turbine (MT), photovoltaic generator (PV), and wind turbine generator (WT), respectively; C gridsell,k,t C is a function of the power transmission from the upper-level power grid to the k-th microgrid at time t; gridbuy,k,t It is a function that enables the k-th microgrid to transmit power to the upper-level grid; It is a 0-1 variable, when When =1, it means P GRID,t <0; when When =0, it means P GRID,t >0; (7) In equation (7), C is a function of the current-day demand for carbon allowances per unit; average,MT This is the industry benchmark value for CO2 emissions per unit output of a micro gas turbine (MT). Let be the output power of the micro gas turbine MT in the k-th microgrid group at time t; Let be the CO2 emission coefficient of the micro gas turbine MT in the kth microgrid in the microgrid group; (8) In equation (8), C k,ADR For the k-th microgrid in the microgrid group, call the function of type A demand-side response (ADR), P k,ADR,t Let A be the amount of Type A demand-side response (ADR) invoked by the k-th microgrid at time t; Step 1.3: Construct the power balance constraint at time t during the day-ahead phase using equation (2): (2) In equation (2), P load,t P represents the total load of the microgrid at time t; WT,t P represents the output power of the wind turbine generator set WT at time t; PV, t P represents the output power of the photovoltaic generator set at time t; ADR,t P represents the amount of Type A demand-side response (ADR) participating in scheduling at time t; MT,t This represents the output power of the micro gas turbine MT at time t; It is a 0-1 variable, when A value of 1 indicates that the battery is charging; when A value of 0 indicates that the battery is discharging; P BA,t Let BA be the output power of the battery at time t; For the charging efficiency of the storage battery; P represents the discharge efficiency of the battery. GRID,t Let t be the power interaction between the upper-level grid GRID and the microgrid group at time t; Step 1.4: Construct the output constraints of each micro-source at time t during the day-ahead phase using equation (3): (3) In equation (3), Let represent the minimum output constraint of the i-th micro-source at time t. This represents the maximum output constraint of the i-th micro-power source at time t; Step 1.5: Construct the requirement-side response call volume constraint for the current-day phase A using equation (4): (4) In equation (4), Let t be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let be the maximum value of the type A demand-side response (ADR) that can be invoked in the microgrid group at time t; Step 2: Calculate the daily carbon quota pre-allocation based on the microgrid group's day-ahead dispatch results, carbon quota demand, and waste amount; Step 2.1: Based on the day-ahead scheduling results of the microgrid group and the carbon quota demand and discard amount, calculate the carbon quota pre-allocation amount at time t within the day using equation (9). : (9) In equation (9), t in t represents the time scale for intraday scheduling of microgrid groups. ahead This indicates the time scale for day-ahead scheduling of microgrid groups. This represents the output power of the i-th micro-source in the k-th microgrid in the microgrid group at time t; Step 3: Based on the day-ahead scheduling results of the microgrid group and the pre-allocated carbon quota for each time period during the day, with the minimum intraday loss as the objective function and power balance, output, and demand-side response call amount as constraints, construct and solve the intraday optimization scheduling model of the microgrid group to obtain the intraday scheduling results of the microgrid group.

2. The microgrid group multi-timescale optimal scheduling method considering carbon trading and demand-side response as described in claim 1, characterized in that, Step 3 includes: Step 3.1: Construct the intraday objective function with the goal of minimizing the losses of the microgrid group using equation (10). : (10) In equation (10), The invocation overhead for the Class A demand-side response (ADR) invoked at time t, as determined in the previous phase; f k,t This represents the operating loss of the k-th microgrid in the microgrid group at time t; This represents the intraday carbon trading loss of the k-th microgrid in the microgrid group at time t; This represents the loss of the k-th microgrid in the microgrid group when it invokes the B-type demand-side response (BDR) at time t; Step 3.2: Construct the power balance constraint at time t during the intraday phase using equation (11): (11) In equation (11), P BDR,t This represents the amount of type B demand-side response that participates in scheduling at time t; Step 3.3: Construct the output constraint of the i-th micro-power source at time t during the intraday phase using equation (12): (12) Step 3.4: Construct the intraday A-side demand response ADR call volume constraint using equation (13): (13) In equation (13), Let t be the minimum value of the type A demand-side response (ADR) that can be called in the microgrid group at time t; Let be the maximum value of the type A demand-side response (ADR) that can be invoked in the microgrid group at time t; Step 3.5: Construct the intraday phase A-type demand-side response (BDR) call volume constraint using equation (14): (14) In equation (14), Let t be the minimum value of the type B demand-side response (BDR) that can be called in the microgrid group at time t; Let t be the maximum value of the B-type demand-side response (BDR) that can be called in the microgrid group at time t.

3. The microgrid group multi-timescale optimization scheduling method considering carbon trading and demand-side response as described in claim 2, characterized in that, In step 3.1 From equation (15), we get: (15) In equation (15), The coefficient function for the unit carbon allowance required within the day; In step 3.1 From equation (16), we get: (16) In equation (16), C k,BDR To invoke the coefficient function of the Type B Demand-Side Response (BDR) for the k-th microgrid in the microgrid group, P k,BDR,t Let be the amount of the Type B Demand Response (BDR) invoked by the k-th microgrid in the microgrid group at time t.

4. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing the microgrid group multi-time-scale optimization scheduling method according to any one of claims 1-3, and the processor is configured to execute the programs stored in the memory.

5. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the multi-time-scale optimization scheduling method for microgrid groups according to any one of claims 1-3.

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