Layered and distributed robust operation method of power distribution network-integrated energy system for low-carbon scheduling

Abstract

The invention discloses a low-carbon scheduling-oriented power distribution network-integrated energy system layered distributed robust operation method, which comprises the following steps of: 1, establishing a carbon coupling electricity price mechanism-based IES-power distribution network multi-layer low-carbon cooperative operation framework in order to realize low-carbon interaction between a power distribution network and different operation main bodies of an IES; 2, in order to effectively support a hierarchical optimization decision on a physical execution level of the power distribution network, constructing a power distribution network low-carbon operation model containing a reconfigurable intelligent soft switch R-SOP; 3, propelling a system carbon reduction target from the angle of multi-energy flow coupling and cooperation, and constructing an IES operation model containing multiple energy devices; and 4, considering the uncertainty of new energy output, and constructing an IES-power distribution network layered distributed robust optimization strategy. According to the method, the interaction efficiency is ensured by constructing a multi-layer low-carbon cooperative operation framework of the power distribution network-IES and utilizing the feeder line-port dynamic recombination capability of the R-SOP; and an ATC-DRO algorithm is constructed to cope with privacy problems of different subjects and randomness of new energy output.

Description

Technical Field

[0001] This invention belongs to the field of flexible distribution network operation optimization with integrated energy system, specifically a hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch. Background Technology

[0002] In recent years, integrated energy systems (IES), as autonomous entities capable of coordinating multiple heterogeneous source-load resources, have been widely deployed in distribution networks (DN). IES, through source-grid-load-storage coordinated control and multi-energy complementarity mechanisms, actively promotes the grid connection of new energy sources and integrates various energy forms such as electricity, heat, and gas, achieving economical, efficient, and clean operation, thus becoming an important direction in current energy system research. Flexible planning of distributed resources such as smart soft open points (SOPs) and energy storage can effectively balance overall flexibility and economy. Reconfigurable soft open points (R-SOPs), compared to multi-terminal SOPs of the same capacity, can switch feeders, adjust port capacity, and improve active power transmission efficiency. Therefore, R-SOPs have a greater advantage than traditional SOPs in facilitating interactive control of distribution networks.

[0003] However, traditional R-SOPs rely on mechanical switches for dynamic switching of port capacity. Limited by their operating speed and response accuracy, they struggle to meet the demands for rapid power flow regulation in complex power distribution scenarios. Therefore, it is imperative to improve R-SOPs, enhancing their operating speed and control accuracy to quickly respond to and dynamically adjust power transmission paths. This will effectively guide low-carbon energy flow to high-demand areas, thereby optimizing the allocation of carbon emission responsibilities and further enhancing the low-carbon efficiency and flexibility of power distribution network operation.

[0004] Meanwhile, to address the multi-layered collaborative operation of distribution networks and Integrated System Engineering (IES), distributed algorithms are often employed to optimize solutions among different stakeholders. Distributed optimization algorithms such as the alternating direction multiplier method, consensus algorithms, and analytical target cascading (ATC) each have their own advantages in this field. Among them, ATC offers greater flexibility in coordinating subproblems, allowing various types of penalty functions to be applied to different levels of the distribution network and thus to various distributed solutions. However, distribution network and IES interaction strategies based on deterministic scenarios can exhibit excessive scheduling deviations when facing uncertainties in renewable energy scenarios. Therefore, existing deterministic analysis strategies are difficult to apply to the interaction analysis of IES-distribution networks considering the uncertainties of renewable energy. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by proposing a hierarchical distributed robust operation method for distribution networks and integrated energy systems (IES) oriented towards low-carbon dispatch. By optimizing system operation modes, it enhances the flexibility and stability of power flow control in the distribution network, thereby improving the interaction efficiency between the distribution network and IES. It standardizes carbon emission responsibility sharing criteria, promotes deep collaborative carbon reduction between the distribution network and the integrated energy system, and facilitates the transformation of the energy system towards low-carbon and clean energy. Simultaneously, it ensures the economic efficiency of system operation and the reliability in responding to the stochastic characteristics of renewable energy output. This provides technical support for low-carbon dispatch of distribution networks and IES under the new power system context, aligning with the energy low-carbon development strategy and possessing significant engineering application value and environmental significance.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention provides a hierarchical distributed robust operation method for a distribution network-integrated energy system oriented towards low-carbon dispatch, characterized by the following steps: Step 1: Construct a carbon-coupled electrical energy interaction mechanism based on carbon potential; Step 2: Construct the reconfigurable intelligent soft switch R-SOP model; Step 3: Construct the objective function for the operation of the distribution network. ; Step 4: Construct a distribution network operation model including R-SOP; Step 5: Construct an IES operating model containing multiple energy devices; Step 6: Construct the decoupled distribution network and IES operation model; Step 7: Based on the decoupled distribution network and IES operation model, construct a hierarchical distribution network-IES sub-bar operation model and transform it into a two-layer main model and a two-layer sub-model; Step 8: Solve the two-layer main model and the two-layer sub-model to obtain the final operation scheme, which includes the reconfigurable soft switch R-SOP action in the distribution network, the predicted and adjustment actions of each device in the IES, and the interaction actions between the distribution network and the IES.

[0007] The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch described in this invention is also characterized in that, in step 1, the carbon-coupled power interaction mechanism is obtained from equations (1) to (3). (1) (2) (3) In equations (1)-(3), For nodes in the distribution network carbon potential; For inflow node The set of branches; For inflow node The Active power of each branch circuit; For inflow node The Carbon potential of the branch; For generator G-direction node The active power transmitted; For nodes The carbon emission intensity of the generator G connected to the grid; for Carbon potential matrix of time-of-use distribution networks; for Active power flux matrix of nodes in a time-period distribution network; for Line power flow distribution matrix of the time-period distribution network; for Generator output matrix of time-period distribution network; for Carbon emission factor matrix of generator units in different time periods; For any integrated energy system IES access node Carbon-coupled unit interaction characteristic; The unit interactive characteristic quantity of the power output of the distribution network; This refers to the unit interactive characteristic quantity of power received by the distribution network. is the carbon quota coefficient for the distribution network; T represents transpose.

[0008] Furthermore, in step 2, the reconfigurable intelligent soft switch R-SOP model of equations (4)-(11) is used. (4) (5) (6) (7) (8) (9) (10) (11) In equations (4)-(11), , These represent the set of nodes connected to the converter and the set of all converters, respectively. The number of nodes connected to the converter; , , These are the nodes that are connected to R-SOP. All connected converters Total losses, DC power, and AC power during the time period; For nodes to access R-SOP All connected converters Reactive power during a given time period; For nodes to access R-SOP All connected converters Apparent power over a given time period; For converter The capacity; The loss factor for R-SOP; It indicates a converter exist Whether the node is connected to the voltage source converter at any time Connected; For R-SOP, the node connected to the voltage source converter Carbon potential during electrical energy transmission; For R-SOP, the node connected to the voltage source converter The active power transmitted; Nodes connected to the voltage source converter Carbon emission intensity of accessing R-SOP; For R-SOP from the node connected to the voltage source converter Carbon potential when receiving electrical energy.

[0009] Furthermore, in step 3, the objective function for the operation of the distribution network is obtained from equations (12) to (15). : (12) (13) (14) (15) In equations (12)-(15), For the first The probability values ​​of a discrete scenario; For the distribution network in the first The objective function for running in a discrete scenario; This is the cost function for the distribution network to receive power from the upper-level power grid; This is the loss function of the distribution network lines; R-SOP loss function; For the distribution network and all IES interaction functions; Cost function for distribution network participation in the carbon market; This represents the total number of time periods operating under a fixed cycle. Outputting electrical energy to the upper-level power grid The unit interaction characteristic of the time period; In order to coordinate with the upper-level power grid Interaction power over a given time period; For distribution network nodes; This represents the total number of all IES connected to the distribution network. This is the line loss penalty factor; for Time period nodes With nodes Branch roads between The current flowing through it; for Time period nodes With nodes Branch roads between The resistance value; for Time period and the m The unit energy interaction characteristic of each IES; for Time-of-use distribution network and the first m The interaction power of each IES; Characteristic quantity per unit of carbon emissions; Carbon emissions generated on the load side of the distribution network; Carbon quotas for power distribution networks; The total carbon allowance for the distribution network area; for Energy consumption by the distribution network load during specific time periods; for Time-of-use distribution network and the first m The interaction power of each IES.

[0010] Furthermore, in step 4, a distribution network operation model containing R-SOP is constructed using equations (16)-(22): (16) (17) (18) (19) (20) (twenty one) (twenty two) In equations (16)-(22), , for Time period nodes With nodes Branch roads between Active and reactive power; , for Time period nodes With nodes Branch roads between Active and reactive power; for Time period nodes With nodes Branch roads between The reactance value; , for Time period nodes Net injected active and reactive power; , for Time period nodes ,node The magnitude of the voltage; for Time-of-use distribution network nodes and the m The interaction power of each IES; for Time-of-use distribution network nodes The load power; , These are the upper and lower limits of the voltage.

[0011] Furthermore, step 5 includes: Step 5-1: Construct the objective function of IES using equations (24)-(27). : (twenty four) (25) In equations (24)-(25): The objective function for the day-ahead execution of IES; For the IES Adjusting the objective function in a discrete scenario; Let IES be the energy purchase cost function; For device maintenance functions in IES; For the wind and solar power curtailment penalty function of IES; For the first Energy purchase cost adjustment function for IES in discrete scenarios; For the first Equipment maintenance cost adjustment function for IES in discrete scenarios; For the first Adjustment functions for wind curtailment and solar power penalties in discrete scenarios of IES; For the first Carbon trading cost function of IES in discrete scenarios; , For the interaction characteristics of electricity and natural gas; for Power interaction between the distribution network and the m-th IES during the time period; for The amount of natural gas received from the upper-level gas network in the m-th IES of the time period; for Natural gas production from P2G equipment in the m-th IES during time period; D represents the set of all equipment in the IES. For equipment Unit operation and maintenance characteristic quantity; for Time-of-use equipment The output power; This refers to a collection of new energy generating units, including: photovoltaic and wind turbines; For the m-th new energy unit in the IES Maximum active power output; for New energy units in the m-th IES period The actual active power output; For the first In a discrete scenario The interaction power between the m-th IES and the distribution network adjustment in time period m; For the first In a discrete scenario The amount of adjusted natural gas received in the m-th IES during time period; For the first In a discrete scenario The amount of natural gas synthesized by the P2G unit in the m-th IES (electro-to-gas) period is adjusted. For the first In a discrete scenario Time-of-use equipment Adjusted output power; For the first In a discrete scenario New energy units in the m-th IES period The actual adjusted active power output; For the first The actual carbon emissions of the m-th IES in a discrete scenario; For the first The carbon emission allowances for the m-th IES under discrete scenarios, where K represents the total number of discrete scenarios, and we have: (26) (27) In equations (26)-(27): for Time period m The node carbon potential of each IES; , for Time period m Carbon emissions of CHP cogeneration units and GB gas-fired boilers in each IES; for Time period m Carbon capture per IES; Step 5-2: Construct constraint models for the devices in the IES, including: the carbon capture system (CCS), the power-to-gas (P2G) device, the combined heat and power (CHP) unit, the gas boiler (GB), and the energy storage device; where CHP includes: gas turbine (GT) and waste heat boiler (WHB); and the energy storage device includes: battery (BT) and thermal storage tank (HST).

[0012] Furthermore, step 5-2 includes: Step 5-2-1: Construct using equations (28) and (31) Constraint model of time period CCS: (28) (29) (30) (31) In equations (28)-(31): Carbon emission intensity of CCS; This refers to the calorific value of natural gas. for Time period m The amount of natural gas consumed by CCS in each IES; for Time period m Fixed energy consumption of CCS in each IES; For the first m Energy consumption of CCS in each IES; The energy required for CCS to process one unit of carbon dioxide; Step 5-2-2: Construct using equations (31) and (32) The operational model of time-based P2G: (32) (33) In equations (31)-(32): The heat energy that can be converted from a unit of electrical energy; For P2G working efficiency; for Time period m The electrical energy consumed by P2G in each IES; for Time period m The amount of carbon dioxide consumed by P2G in each IES; The amount of carbon dioxide required to generate one unit of natural gas; Step 5-2-3: Construct using equations (31) and (32) Constraint model for CHP units during specific time periods: (34) (35) In equations (34)-(35): , , Three carbon emission coefficients for gas-fired equipment; for Power generation of CHP during the period; for Heating power of CHP during the specified time period; The power generation efficiency of CHP; for Natural gas consumed by CHP during the period; for Waste heat from exhaust gas during the CHP period; This refers to the heat loss rate; For waste heat recovery efficiency; The flue gas recovery rate of CHP; Step 5-2-4: Construct using equations (36) and (37) Constraint model for GB-rated gas-fired boilers; (36) (37) In equations (36)-(37): for Heating power of GB during the time period; The heating efficiency is in GB. for Natural gas consumed during the GB period; Step 5-2-5: Construct using equations (38) and (39) Constraint model for time-of-use energy storage devices: (38) (39) In equations (38)-(39): for The state of charge of BT during the time period express The state of charge of BT during the time period; , for The charging and discharging power of BT during the time period; , for The charging and discharging efficiency of BT during the time period; for Thermal energy stored in the HST period; for Thermal energy stored in the HST period; , for The heat storage and release power of the HST period; The energy loss rate of HST; , The heat storage and release efficiency of HST.

[0013] Furthermore, step 6 includes: Step 6-1: Construct the first equation using equation (40). Penalty function of the IES running model after decoupling under the inner loop : (40) In equation (40): , for The multipliers of the linear and quadratic terms of the Lagrange penalty function corresponding to the m-th IES in time period; For the first Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first -1 inner loop Time period distribution network expectation and the first m The interaction power of each IES; Step 6-2: Construct the first equation using equation (41). Penalty function of the decoupled distribution network operation model under the second inner loop : (41) In equation (41), For the first -1 inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first Under the second inner loop Time period distribution network expectation and the first m The interaction power of each IES; Step 6-3: Construct the first equation using equation (42). Decoupling constraints under the inner loop: (42) In equation (42): for Time period m The interaction power value between each IES and the distribution network; This represents the maximum value of the interaction power between the IES and the distribution network.

[0014] Furthermore, step 8 includes the following steps: Step 7-1: Construct a two-layer master model using equations (45) and (46): (45) (46) In equations (45)-(46): The IES objective function for the two-layer master model; The objective function for the distribution network in the two-layer master model; The IES constraint objective for the two-layer sub-model; The distribution network constraint objective for the two-layer sub-model; The coefficients of the objective function; Forecast power output for new energy sources; For the first New energy output values ​​for discrete scenarios; For the first The probability values ​​of a discrete scenario; Step 7-2: Construct a two-layer sub-model using equations (47) and (48): (47) (48) In equations (47)-(48), The IES objective function for the two-layer sub-model; The objective function for the distribution network in the two-layer sub-model; Let be the feasible region of the probability constraint; and we have: (49) In equation (49): Initial probability values ​​for scenarios that contribute power to new energy sources; , These are the permissible deviations of the probability under the constraints of the 1-norm and ∞-norm, respectively.

[0015] Furthermore, step 8 includes the following steps: Step 8-1: Define the number of iterations for the outer loop of the CCG as... Define the number of loop iterations in the ATC inner loop as ;initialization =1; Initialize the first Upper bound of distribution network target under secondary external circulation , No. Lower bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop , No. Lower bound of IES target under secondary outer loop =0 and the Under the second outer loop Probability values ​​of discrete scenarios ;definition To improve the convergence accuracy of the outer loop; Step 8-2: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The IES objective function of a two-level master model under a secondary inner loop. =0; initialize the first The second outer loop Objective function of the distribution network in the double-layer master model under the secondary inner loop =0; Step 8-3: Calculate the first step according to equations (45)-(46). The first iteration under the outer loop The overall objective function of the bi-level master model under the inner loop. IES objective function and its first The second outer loop Under the second inner loop The expected interaction power between the m-th IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period Thus, the first The second outer loop IES Action Set in Sub-Local Loop Including: the The second outer loop Predicted actions of each device in the IES during the second inner loop, as well as the interaction between the distribution network and the IES; Step 8-4: Determine whether equations (50) and (51) are valid. If they are valid, then let the first equation be valid. Lower bound of distribution network target under secondary external circulation , No. Lower bound of IES target under secondary outer loop Then, proceed to step 8-5; otherwise, Assign to After updating the penalty function according to equation (52), return to step 8-3 for sequential execution; (50) (51) (52) In equations (52)-(54), This represents the precision value for convergence condition 1 of the ATC algorithm; This represents the precision value for convergence condition 2 of the ATC algorithm; For the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The first iteration under the outer loop Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first The first iteration under the outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period; Step 8-5: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop IES objective function of the two-level sub-model under the inner loop =0; initialize the first The second outer loop Objective function of distribution network in the two-layer sub-model under the inner loop =0; Step 8-6: Based on Calculate the first one according to equations (53) and (54). Under the second outer loop Probability values ​​of discrete scenarios , No. The first iteration under the outer loop The overall objective function of the bi-level sub-model under the inner loop. and its first The first iteration under the outer loop Under the second inner loop The expected interaction power between the m-th IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period Thus, the first The second outer loop IES Adjustment Action Set under Secondary Loop and the The first iteration under the outer loop Distribution network action scheme set under secondary cycle ,in, Including: the The second outer loop The adjustment actions of each device in the IES during the secondary loop, as well as the interactive adjustment actions between the distribution network and the IES. Including the The second outer loop R-SOP action in the next internal loop; (53) (54) In equations (53) and (54), For the first The second outer loop In the second inner loop, the IES in the bilayer sub-model is at the... k The optimal objective function for a discrete scenario; For the first The second outer loop The distribution network in the two-layer sub-model under the second inner cycle is in the... k The optimal objective function for a discrete scenario; For the first k A set of new energy power outputs in discrete scenarios; Step 8-7: Determine whether equations (55) and (56) are valid. If they are valid, obtain the first... Upper bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop Then, proceed to step 8-8; otherwise, Assign to Then, update the penalty function according to equation (52) and return to steps 8-6 to execute sequentially; (55) (56) Step 8-8: Judgment as well as If the condition is met, then the final operating plan has been obtained. Otherwise, Assign to Then, return to step 8-2 and execute sequentially.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By constructing a multi-layer low-carbon collaborative operation framework for the IES-distribution network, a dual carbon reduction effect is achieved by utilizing carbon potential and carbon-coupled electricity prices. Compared with the multi-layer interaction method based on traditional electricity price-driven approaches, this effectively reduces the overall system carbon emissions and ensures the low-carbon nature of the system. 2. Utilize the feeder-port dynamic reconfiguration capability of R-SOP to establish a flexible distribution network operation model containing R-SOP, and combine it with carbon emission flow theory to optimize carbon emission allocation responsibility. In the interaction process between the distribution network and IES, compared with the traditional SOP to regulate power flow, it reduces distribution network line losses, ensures interaction efficiency, and achieves economic operation of the distribution network. 3. Based on the multi-entity characteristics of the IES-distribution network and the randomness of new energy output, the ATC-DRO algorithm is established. Through a two-layer principal distributed algorithm for exchanging limited information and a two-stage sub-Browser optimization of the multi-layer model, privacy interaction of the IES-distribution network under uncertain conditions is realized. Attached Figure Description

[0017] Figure 1 Framework diagram of hierarchical distributed IES-distribution network low-carbon collaborative operation; Figure 2 Diagram of the improved IEEE 33-node test system; Figure 3 Carbon potential diagram of distribution network nodes; Figure 4 Distribution network and IES power price interaction diagram; Figure 5 IES2 interaction power graphs in different scenarios; Figure 6 R-SOP active power transfer diagram; Figure 7 R-SOP reactive power transfer diagram; Figure 8 R-SOP capacity allocation result diagram; Figure 9 Flowchart of the DRO-ATC algorithm. Detailed Implementation

[0018] In this embodiment, a hierarchical distributed robust operation method for a distribution network-integrated energy system oriented towards low-carbon dispatching is described in the following steps: Step 1: Considering the need to fully leverage the proactive control capabilities of the distribution network and the IES (Environmental Engineering System), as well as the privacy and uncertainty inherent in the collaborative interactions between different entities, this paper proposes using distributed algorithms and the sub-Browser algorithm to jointly optimize the hierarchical operation model. Therefore, a multi-layered low-carbon collaborative operation framework for the IES-distribution network is established as follows: Figure 1 As shown, the carbon-coupled valence mechanism based on carbon potential is obtained from equations (1)-(3): (1) (2) (3) In equations (1)-(3), For nodes in the distribution network carbon potential; For inflow node The set of branches; For inflow node The Active power of each branch circuit; For inflow node The Carbon potential of the branch; For generator G-direction node The active power transmitted; For nodes The carbon emission intensity of the generator G connected to the grid; for Carbon potential matrix of time-of-use distribution networks; for Active power flux matrix of nodes in a time-period distribution network; for Line power flow distribution matrix of the time-period distribution network; for Generator output matrix of time-period distribution network; for Carbon emission factor matrix of generator units in different time periods; For any integrated energy system IES access node The carbon coupling unit interaction characteristic is calculated by equation (3); The unit interactive characteristic of the power output from the distribution network is obtained in Table 1; The unit interactive characteristic of the power received by the distribution network is obtained in Table 1; is the carbon quota coefficient for the distribution network; T represents transpose.

[0019] Table 1. Unit interactive characteristics of output power and received power in the distribution network

[0020] Step 2: Propose R-SOP, which consists of a voltage source converter (VSC) and a relay switch. Its basic principle is that the relay switch uses level signals to achieve remote control, reconstruct the connection relationship between each VSC port and dynamically adjust the port capacity allocation ratio. The R-SOP model is constructed by equations (4)-(11): (4) (5) (6) (7) (8) (9) (10) (11) In equations (4)-(11), , These represent the set of nodes connected to the converter and the set of all converters, respectively. The number of nodes connected to the converter; , , These are the nodes that are connected to R-SOP. All connected converters Total losses, DC power, and AC power during the time period; For nodes to access R-SOP All connected converters Reactive power during a given time period; For nodes to access R-SOP All connected converters Apparent power over a given time period; For converter The capacity; The loss factor for R-SOP; It indicates a converter exist Whether the node is connected to the voltage source converter at any time Connected; For R-SOP, the node connected to the voltage source converter Carbon potential during electrical energy transmission; For R-SOP, the node connected to the voltage source converter The active power transmitted; Nodes connected to the voltage source converter Carbon emission intensity of accessing R-SOP; For R-SOP from the node connected to the voltage source converter Carbon potential when receiving electrical energy.

[0021] Step 3: In the real-time phase of the distribution network, the objective is to minimize the sum of the daily operating costs and carbon market costs. The objective function of the distribution network is obtained from equations (12) to (15). : (12) (13) (14) (15) In equations (12)-(15), For the first The probability values ​​of a discrete scenario; For the first distribution network Run the objective function in a discrete scenario; This is the cost function for the distribution network to receive power from the upper-level power grid; This is the loss function of the distribution network lines; R-SOP loss function; For the distribution network and all IES interaction functions; Cost function for distribution network participation in the carbon market; This represents the total number of time periods operating under a fixed cycle. Outputting electrical energy to the upper-level power grid The unit interaction characteristic of the time period; In order to coordinate with the upper-level power grid Interaction power over a given time period; For distribution network nodes; This represents the total number of all IES connected to the distribution network. This is the line loss penalty factor; for Time period nodes With nodes Branch roads between The current flowing through it; for Time period nodes With nodes Branch roads between The resistance value; for Time period and the m The unit energy interaction characteristic of each IES; for Time-of-use distribution network and the first m The interaction power of each IES; Characteristic quantity per unit of carbon emissions; Carbon emissions generated on the load side of the distribution network; Carbon quotas for power distribution networks; The total carbon allowance for the distribution network area; for Energy consumption by the distribution network load during specific time periods; for Time-of-use distribution network and the first m The interaction power of each IES.

[0022] Step 4: Construct a distribution network operation model containing R-SOP using equations (16)-(22): (16) (17) (18) (19) (20) (twenty one) (twenty two) In equations (16)-(22), , for Time period nodes With nodes Branch roads between Active and reactive power; , for Time period nodes With nodes Branch roads between Active and reactive power; for Time period nodes With nodes Branch roads between The reactance value; , for Time period nodes Net injected active and reactive power; , for Time period nodes ,node The magnitude of the voltage; for Time-of-use distribution network nodes and the m The interaction power of each IES; for Time-of-use distribution network nodes The load power; , These are the upper and lower limits of the voltage.

[0023] Step 5: Construct an IES operating model containing multiple energy devices: Step 5-1: Construct the objective function of IES using equations (24)-(27). : (twenty four) (25) In equations (24)-(25): The objective function for the day-ahead execution of IES; The intraday adjustment objective function for IES; Let IES be the energy purchase cost function; For device maintenance functions in IES; For the wind and solar power curtailment penalty function of IES; For the first Energy purchase cost adjustment function for IES in discrete scenarios; For the first Equipment maintenance cost adjustment function for IES in discrete scenarios; For the first Adjustment functions for wind curtailment and solar power penalties in discrete scenarios of IES; For the first Carbon trading cost function of IES in discrete scenarios; , For the interaction characteristics of electricity and natural gas; for Power interaction between the distribution network and the m-th IES during the time period; for The amount of natural gas received from the upper-level gas network in the m-th IES of the time period; for Natural gas production from P2G equipment in the m-th IES during time period; D represents the set of all equipment in the IES. For equipment Unit operation and maintenance characteristic quantity; for Time-of-use equipment The output power; This refers to a collection of new energy generating units, including: photovoltaic and wind turbines; For the m-th new energy unit in the IES Maximum active power output; for New energy units in the m-th IES period The actual active power output; For the first In a discrete scenario The interaction power between the m-th IES and the distribution network adjustment in time period m; For the first In a discrete scenario The amount of adjusted natural gas received in the m-th IES during time period; For the first In a discrete scenario The amount of natural gas synthesized by the P2G unit in the m-th IES (electro-to-gas) period is adjusted. For the first In a discrete scenario Time-of-use equipment Adjusted output power; For the first In a discrete scenario New energy units in the m-th IES period The actual adjusted active power output; For the first The actual carbon emissions of the m-th IES in a discrete scenario; For the first The carbon emission allowances for the m-th IES under discrete scenarios, where K represents the total number of discrete scenarios, and we have: (26) (27) In equations (26)-(27): for Time period m The node carbon potential of each IES; , for Time period m Carbon emissions of CHP cogeneration units and GB gas-fired boilers in each IES; for Time period m Carbon capture per IES.

[0024] Step 5-2: Construct constraint models for the devices in the IES, including: the carbon capture system (CCS), the power-to-gas (P2G) device, the combined heat and power (CHP) unit, the gas boiler (GB), and the energy storage device; where CHP includes: gas turbine (GT) and waste heat boiler (WHB); and the energy storage device includes: battery (BT) and thermal storage tank (HST).

[0025] Step 5-2-1: Construct using equations (28) and (31) Constraint model of time period CCS: (28) (29) (30) (31) In equations (28)-(31): Carbon emission intensity of CCS; This refers to the calorific value of natural gas. for Time period m The amount of natural gas consumed by CCS in each IES; for Time period m Fixed energy consumption of CCS in each IES; For the first m Energy consumption of CCS in each IES; The energy required for CCS to process one unit of carbon dioxide.

[0026] Step 5-2-2: Construct using equations (31) and (32) The operational model of time-based P2G: (32) (33) In equations (31)-(32): The heat energy that can be converted from a unit of electrical energy; For P2G working efficiency; for Time period m The electrical energy consumed by P2G in each IES; for Time period m The amount of carbon dioxide consumed by P2G in each IES; The amount of carbon dioxide required to generate one unit of natural gas power.

[0027] Step 5-2-3: Construct using equations (31) and (32) Constraint model for CHP units during specific time periods: (34) (35) In equations (34)-(35): , , Three carbon emission coefficients for gas-fired equipment; for Power generation of CHP during the period; for Heating power of CHP during the specified time period; The power generation efficiency of CHP; for Natural gas consumed by CHP during the period; for Waste heat from exhaust gas during the CHP period; This refers to the heat loss rate; For waste heat recovery efficiency; The flue gas recovery rate of CHP.

[0028] Step 5-2-4: Construct using equations (36) and (37) Constraint model for GB-rated gas-fired boilers; (36) (37) In equations (36)-(37): for Heating power of GB during the time period; The heating efficiency is in GB. for Natural gas consumed during the GB period.

[0029] Step 5-2-5: Construct using equations (38) and (39) Constraint model for time-of-use energy storage devices: (38) (39) In equations (38)-(39): for The state of charge of BT during the time period express The state of charge of BT during the time period; , for The charging and discharging power of BT during the time period; , for The charging and discharging efficiency of BT during the time period; for Thermal energy stored in the HST period; for Thermal energy stored in the HST period; , for The heat storage and release power of the HST period; The energy loss rate of HST; , The heat storage and release efficiency of HST.

[0030] Step 6: Construct the decoupled distribution network and IES operation model using the ATC algorithm; Step 6-1: Construct the first equation using equation (40). Penalty function of the IES running model after decoupling under the inner loop : (40) In equation (40): , for The multipliers of the linear and quadratic terms of the Lagrange penalty function corresponding to the m-th IES in time period; For the first Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first -1 inner loop Time period distribution network expectation and the firstm The interaction power of each IES.

[0031] Step 6-2: Construct the first equation using equation (41). Penalty function of the decoupled distribution network operation model under the second inner loop : (41) In equation (41), For the first -1 inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first Under the second inner loop Time period distribution network expectation and the first m The interaction power of each IES.

[0032] Step 6-3: Construct the first equation using equation (42). Decoupling constraints under the inner loop: (42) In equation (42): for Time period m The interaction power value between each IES and the distribution network; This represents the maximum value of the interaction power between the IES and the distribution network.

[0033] Step 7: Based on the decoupled distribution network and IES operation model, construct a hierarchical distribution network-IES sub-bar operation model and transform it into a two-layer main model and a two-layer sub-model; Step 7-1: Construct the two-layer main model of the CCG algorithm using equations (45)-(46): (45) (46) In equations (45)-(46): The IES objective function for the two-layer master model; The objective function for the distribution network in the two-layer master model; The IES constraint objective for the two-layer sub-model; The distribution network constraint objective for the two-layer sub-model; The coefficients of the objective function; Forecast power output for new energy sources; For the first New energy output values ​​for discrete scenarios; For the first The probability value of a discrete scenario.

[0034] Step 7-2: Construct a two-layer sub-model of the CCG algorithm using equations (47) and (48): (47) (48) In equations (47)-(48), The IES objective function for the two-layer sub-model; The objective function for the distribution network in the two-layer sub-model; The set of variables for the IES output by the two-layer master model is known in this formula and includes: the optimal actions of each device in the IES and the optimal interaction actions between the distribution network and the IES. The set of variables for the two-layer sub-model IES is the unknown set in this formula, and includes: the optimal adjustment actions of each device in the IES and the optimal adjustment interaction actions between the distribution network and the IES; The set of variables for the distribution network in the two-layer sub-model is the unknown set in this formula, including: the optimal control action of R-SOP; to ensure that the probability values ​​of discrete scenarios fluctuate within a reasonable range and are closer to the distribution characteristics of actual operating data, this paper constructs a comprehensive norm composed of 1-norm and ∞-norm as a constraint condition to limit the probability distribution of new energy scenarios, and thus uses equation (49) to obtain the feasible region of probability constraints. : (49) In equation (49): Initial probability values ​​for scenarios that contribute power to new energy sources; , These are the permissible deviations of the probability under the constraints of the 1-norm and ∞-norm, respectively.

[0035] Step 7-3: The model solutions for different scenarios are independent, and the two-layer models of IES and distribution network in each scenario can be solved using the ATC algorithm. Therefore, the sub-model can be solved by combining the parallel solution of the two-layer model. Finally, the variant form of the two-layer sub-model is constructed using equations (50)-(51): (50) (51) In equations (50)-(51): , The optimal objectives for IES and distribution network in the two-layer sub-model are defined in all discrete scenarios.

[0036] Step 8, as follows Figure 9As shown, the two-layer main model and the two-layer sub-model are solved to obtain the operation scheme, which includes the reconfigurable soft switch R-SOP action of the distribution network, the predicted and adjustment actions of each device in the IES, and the interactive actions of the distribution network and the IES.

[0037] Step 8-1: Define the number of iterations for the outer loop of the CCG as... Define the number of loop iterations in the ATC inner loop as ;initialization =1, initialize the first Upper bound of distribution network target under secondary external circulation , No. Lower bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop , No. Lower bound of IES target under secondary outer loop =0 and the Under the second outer loop Probability values ​​of discrete scenarios ;definition This is for the convergence accuracy of the outer loop.

[0038] Step 8-2: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The IES objective function of a two-level master model under a secondary inner loop. Set to 0; initialize the first The second outer loop Objective function of the distribution network in the double-layer master model under the secondary inner loop =0.

[0039] Step 8-3: Calculate the first step according to equations (45)-(46). The first iteration under the outer loop The overall objective function of the bi-level master model under the inner loop. IES objective function and its first The second outer loop Under the second inner loop The expected interaction power between the m-th IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period Thus, the first The second outer loop IES Action Set in Sub-Local Loop Including: the The second outer loop Predictive actions of each device in the IES during the secondary loop, as well as interactive actions between the distribution network and the IES.

[0040] Step 8-4: Determine whether equations (52) and (53) are valid. If they are valid, then let the first equation be valid. Lower bound of distribution network target under secondary external circulation , No. Lower bound of IES target under secondary outer loop Then proceed to step 8-5; otherwise, Assign to After updating the penalty function according to equation (54), return to step 8-3 for sequential execution; (52) (53) (54) In equations (52)-(54), This represents the precision value for convergence condition 1 of the ATC algorithm; This represents the precision value for convergence condition 2 of the ATC algorithm; For the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The first iteration under the outer loop Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first The first iteration under the outer loop Under the second inner loop Time period distribution network expectation and the first m The interaction power of each IES.

[0041] Step 8-5: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop IES objective function of the two-level sub-model under the inner loop Set to 0; initialize the first The second outer loop Objective function of distribution network in the two-layer sub-model under the inner loop =0.

[0042] Step 8-6: Based on Calculate the first according to equations (50)-(51) Under the second outer loop Probability values ​​of discrete scenarios , No. The first iteration under the outer loop The overall objective function of the bi-level sub-model under the inner loop. and its first The first iteration under the outer loop Under the second inner loop Time period m The expected interaction power between each IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop Time period distribution network expectation and the first m Interaction power of each IES Thus, the first The second outer loop IES Adjustment Action Set under Secondary Loop and the The first iteration under the outer loop Distribution network action scheme set under secondary cycle ,in, Including: the The second outer loop The adjustment actions of each device in the IES during the secondary loop, as well as the interactive adjustment actions between the distribution network and the IES. Including the The second outer loop R-SOP action in the next inner loop.

[0043] Step 8-7: Determine whether equations (55) and (56) are valid. If they are valid, obtain the following: Upper bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop Then proceed to step 8-8; otherwise, Assign to Then, update the penalty function according to equation (54) and return to steps 8-6 to execute sequentially; (55) (56) Step 8-8: Judgment as well as If the condition is met, then the final operational plan for the distribution network R-SOP action, the predicted and adjustment actions of each device in the IES, and the interaction actions between the distribution network and the IES are obtained. Otherwise, Assign to Then, return to step 8-2 and execute sequentially.

[0044] Using the above method, the hierarchical distributed robust operation model of the distribution network-integrated energy system for low-carbon dispatch is transformed into an inner and outer two-layer model and then solved to obtain the operation scheme of the distribution network-integrated energy system, including the reconfigurable soft switch R-SOP action.

[0045] 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.

[0046] 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.

[0047] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components: like Figure 2 In the test system shown, the IES access nodes are 32, 24, 10, and 21, and the R-SOP access nodes are 9, 18, 22, and 33, with a total capacity of 3MW. The capacity ratio of each port is divided according to the golden ratio. Upper and lower limit voltages are specified. , The values ​​are set to 1.05 pu and 0.95 pu respectively, the target coefficient for power loss is set to 0.08, and the R-SOP loss coefficient is set to 0.02.

[0048] To fully demonstrate the effectiveness of the hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch, the case study section sets up four schemes (Case 1, Case 2, Case 3, and Case 4) for comparison.

[0049] Case 1: Consider the R-SOP flexible distribution network collaborative model with multiple integrated energy autonomous entities, and use a multi-layer low-carbon collaborative operation framework for optimized scheduling.

[0050] Case 2: Consider the R-SOP flexible distribution network collaborative model with multiple integrated energy autonomous entities, without adopting a multi-layer low-carbon collaborative operation framework, and only achieving multi-layer optimization by transmitting power information.

[0051] Case 3: Consider a flexible distribution network collaborative model with multiple integrated energy autonomous entities and multiple port SOPs, and use a multi-layer low-carbon collaborative operation framework for optimized scheduling.

[0052] Case 4: Consider the collaborative model of a flexible distribution network with multiple integrated energy autonomous entities in the R-SOP, and solve it using a centralized low-carbon algorithm.

[0053] All numerical simulations in the examples section were performed using the YALMIP toolbox in MATLAB_R2024a, with the ATC-DRO algorithm solved by calling the GUROBI12.0 solver.

[0054] exist Figure 2 In the IEEE 33-node test system shown, the above four schemes were executed respectively, and the relevant results of Case 1, Case 2, Case 3 and Case 4 were obtained, as shown in Table 2 and Table 3.

[0055] Table 2

[0056] Table 3

[0057] The comparison results in Table 2 show that Case I, by introducing a multi-layered low-carbon collaborative operation mechanism between the IES and the distribution network, not only changed the power interaction mode between the IES and the external power grid, but also profoundly affected the energy supply structure within the IES, suppressing the operation of natural gas units and reducing their consumption by 33.91% compared to Case II, significantly reducing dependence on fossil fuels. Simultaneously, because the energy interaction process can accurately follow carbon potential and carbon-coupled electricity price signals, the overall carbon emissions of the system are further reduced by 44.53 tons, fully demonstrating the advanced nature of low-carbon optimization in suppressing the operation of high-carbon units and reducing carbon emissions.

[0058] As can be seen from the comparison results in Table 3, compared with Case III, Case I not only has lower R-SOP losses than SOP losses, but also still reduces the total target by 7.6%. Furthermore, because R-SOP can further improve the active power flow control capability of the distribution network by flexibly switching port capacity, it optimizes the power flow distribution in the distribution network, reduces line losses by 47.87%, and reduces the electricity purchased from the transmission network by 2.54MW. In addition, because R-SOP has a certain voltage regulation capability, it reduces the voltage deviation of the distribution network, with the maximum bias value controlled within ±0.0228pu. Moreover, compared with the centralized algorithm, the distributed algorithm reduces the target calculated using the method proposed in this paper by approximately 433.53 yuan, the average and median voltage values ​​are closer to the standard voltage values, the voltage distribution is more concentrated, and it better meets the safety requirements of the distribution network. Therefore, the distributed interaction method based on IES and the distribution network is better able to achieve the overall safety requirements of the distribution network.

[0059] Figure 3 , Figure 4 This paper presents the carbon potential distribution and electricity price of each node at different time periods, calculated based on carbon emission flow theory, under discrete scenario 1 with the highest initial probability. Influenced by renewable energy output and power flow paths, the carbon potential exhibits dynamic fluctuations in both spatial and temporal dimensions. This carbon potential, calculated based on power flow distribution, not only provides a quantitative basis for measuring the carbon emission intensity of energy consumption at each node but also supports a dynamic electricity price mechanism.

[0060] Depend on Figure 5It is evident that the interaction power optimization results of Case II deviate from the carbon emission levels at different time periods. This is because the dynamic changes in node carbon potential and carbon-coupled electricity price are not considered. Although Case II can meet the basic collaborative operation requirements between IES and the distribution network, its carbon reduction effect is limited, failing to fully unleash the system's low-carbon potential. In contrast, Case I introduces a multi-layered low-carbon collaborative operation framework, which can flexibly adjust the interaction power according to the temporal characteristics of carbon potential and carbon-coupled electricity price. For example, at node 24 where IES2 is located, during the low carbon potential and low electricity price period from 01:00 to 05:00, IES2 purchases more low-carbon electricity from the distribution network; while during the high carbon potential and high electricity price periods from 06:00 to 11:00 and 17:00 to 18:00, the purchase amount is significantly reduced; meanwhile, during the period of higher carbon potential from 11:00 to 16:00, IES2 sells more electricity to the distribution network. These dynamic adjustments not only demonstrate sensitivity and responsiveness to both carbon potential and electricity price signals, but also achieve precise energy flow regulation in operational results, enabling low-carbon electricity to prioritize meeting load demand, thereby effectively improving the overall benefits of collaborative carbon reduction. Compared to Case II, Case I not only achieves economic optimization but also demonstrates a significant improvement in carbon emission reduction, validating the superiority and advancement of the proposed multi-layered low-carbon collaborative operation framework in multi-entity scenarios.

[0061] Depend on Figure 6 , Figure 7 and Figure 8 It is evident that, facing frequent changes in energy supply and demand in flexible distribution networks with multiple integrated energy autonomous entities, R-SOP can switch port capacities at any time, improving the flexibility of the distribution network's proactive power flow control capabilities. Referring to Figure D3 in Appendix D, taking the IES1 node as an example, during 02:00-07:00 and 19:00-21:00, IES1 needs to purchase energy from the distribution network, requiring the distribution network to output energy to IES1. Therefore, the R-SOP33 interface, which is closest to IES1, receives the largest capacity allocation. As IES1 begins to transmit energy back to the distribution network, the power flow output from the R-SOP33 interface decreases, and it gradually begins to input energy to other R-SOP interfaces. Similarly, the coordination between the IES4 node and the R-SOP22 interface demonstrates that R-SOP can flexibly respond to changes in the energy interaction between IES and the distribution network, enhancing the flexibility of the distribution network.

[0062] Furthermore, due to the increased load on nodes surrounding node 33 between 08:00-10:00 and 15:00-19:00, the R-SOP33 interface received the largest capacity allocation and had the largest reactive power transmission, demonstrating that while responding to changes in IES (Environmentally Independent Systems), the R-SOP did not lose its ability to regulate the regular load changes in the distribution network. The above analysis indicates that the R-SOP can promptly and proactively adjust power flow in flexible distribution networks with multiple integrated energy autonomous entities, quickly addressing IES demands and reducing the impact of IES power supply and consumption fluctuations on distribution network security.

[0063] To better verify the characteristics and advantages of the proposed hierarchical distributed robust optimization method compared with other methods, this section sets up five sets of scenarios for comparison, and the results are shown in Table 3: Scenario 1: Deterministic programming for a hierarchical system; Scenario 2: Stochastic programming for a hierarchical system; Scenario 3: Robust optimization for a hierarchical system; Scenario 4: Robust optimization for a hierarchical system.

[0064] Table 4

[0065] As shown in Table 4, compared with scenarios 2 and 3, scenario 1 does not consider the uncertainty of renewable energy output, and its target is the most ideal and the lowest. Analyzing examples 2 and 3, the total RO target is higher than that of ATC-DRO and SO, and the IES curtailment targets of RO and ATC-DRO are both 0. This indicates that RO and ATC-DRO can handle the uncertainty of renewable energy output well, but RO considers a more extreme scenario, so its IES and distribution network targets are higher than those of ATC-DRO.

[0066] Comparing scenarios 2 and 4, SO (Solar Energy Depletion) cannot fully absorb the renewable energy output in the Energy Saving Environment (IES), requiring it to purchase more electricity from the distribution network. This results in the ATC-DRO IES operating target being lower than SO, while the distribution network operating target is higher. In contrast, the ATC-DRO method is more conservative, thus its overall target is higher than SO. Overall, although the ATC-DRO overall target is higher than SO, ATC-DRO can avoid the curtailment target and has a stronger capacity for absorbing renewable energy.

[0067] In summary, the DRO method adopted in this invention fully considers the impact of various uncertainties on system planning. The results show that the ATC-DRO method achieves a good balance between economy and robustness.

Claims

1. A hierarchical distributed robust operation method for a distribution network-integrated energy system oriented towards low-carbon dispatch, characterized in that, Includes the following steps: Step 1: Construct a carbon-coupled electrical energy interaction mechanism based on carbon potential; Step 2: Construct the reconfigurable intelligent soft switch R-SOP model; Step 3: Construct the objective function for the operation of the distribution network. ; Step 4: Construct a distribution network operation model including R-SOP; Step 5: Construct an IES operating model containing multiple energy devices; Step 6: Construct the decoupled distribution network and IES operation model; Step 7: Based on the decoupled distribution network and IES operation model, construct a hierarchical and sub-bar operation model of distribution network-IES and transform it into a two-layer main model and a two-layer sub-model; Step 8: Solve the two-layer main model and the two-layer sub-model to obtain the final operation scheme, which includes the reconfigurable soft switch R-SOP action in the distribution network, the predicted and adjustment actions of each device in the IES, and the interaction actions between the distribution network and the IES.

2. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 1, characterized in that, In step 1, the carbon-coupled electrical energy interaction mechanism is obtained from equations (1) to (3): (1) (2) (3) In equations (1)-(3), For nodes in the distribution network carbon potential; For inflow node The set of branches; For inflow node The Active power of each branch circuit; For inflow node The Carbon potential of the branch; For generator G-direction node The active power transmitted; For nodes The carbon emission intensity of the generator G connected to the grid; for Carbon potential matrix of time-of-use distribution networks; for Active power flux matrix of nodes in a time-period distribution network; for Line power flow distribution matrix of the time-period distribution network; for Generator output matrix of time-period distribution network; for Carbon emission factor matrix of generator units in different time periods; For any integrated energy system IES access node Carbon-coupled unit interaction characteristic; The unit interactive characteristic quantity of the power output of the distribution network; This refers to the unit interactive characteristic quantity of power received by the distribution network. is the carbon quota coefficient for the distribution network; T represents transpose.

3. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 2, characterized in that, Step 2 utilizes the reconfigurable intelligent soft switch R-SOP model based on equations (4) to (11): (4) (5) (6) (7) (8) (9) (10) (11) In equations (4)-(11), , These represent the set of nodes connected to the converter and the set of all converters, respectively. The number of nodes connected to the converter; , , These are the nodes that are connected to R-SOP. All connected converters Total losses, DC power, and AC power during the time period; For nodes to access R-SOP All connected converters Reactive power during a given time period; For nodes to access R-SOP All connected converters Apparent power over a given time period; For converter The capacity; The loss factor for R-SOP; It indicates a converter exist Whether a node is connected to the voltage source converter at any given time Connected; For R-SOP, the node connected to the voltage source converter Carbon potential during electrical energy transmission; For R-SOP, the node connected to the voltage source converter The active power transmitted; Nodes connected to the voltage source converter Carbon emission intensity of accessing R-SOP; For R-SOP from the node connected to the voltage source converter Carbon potential when receiving electrical energy.

4. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 3, characterized in that, In step 3, the objective function for the operation of the distribution network is obtained from equations (12) to (15). : (12) (13) (14) (15) In equations (12)-(15), For the first The probability values ​​of a discrete scenario; For the distribution network in the first The objective function for running in a discrete scenario; This is the cost function for the distribution network to receive power from the upper-level power grid; This is the loss function of the distribution network lines; R-SOP loss function; For the distribution network and all IES interaction functions; Cost function for distribution network participation in the carbon market; This represents the total number of time periods operating under a fixed cycle. Outputting electrical energy to the upper-level power grid The unit interaction characteristic of the time period; In order to coordinate with the upper-level power grid Interaction power over a given time period; For the set of distribution network nodes; This represents the total number of all IES connected to the distribution network. This is the line loss penalty factor; for Time period nodes With nodes Branch roads between The current flowing through it; for Time period nodes With nodes Branch roads between The resistance value; for Time period and the m The unit energy interaction characteristic of each IES; for Time-of-use distribution network and the first m The interaction power of each IES; Characteristic quantity per unit of carbon emissions; Carbon emissions generated on the load side of the distribution network; Carbon quotas for power distribution networks; The total carbon allowance for the distribution network area; for Energy consumption by the distribution network load during specific time periods; for Time-of-use distribution network and the first m The interaction power of each IES.

5. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 4, characterized in that, In step 4, a distribution network operation model containing R-SOP is constructed using equations (16) to (22): (16) (17) (18) (19) (20) (21) (22) In equations (16)-(22), , for Time period nodes With nodes Branch roads between Active and reactive power; , for Time period nodes With nodes Branch roads between Active and reactive power; for Time period nodes With nodes Branch roads between The reactance value; , for Time period nodes Net injected active and reactive power; , for Time period nodes ,node The magnitude of the voltage; for Time-of-use distribution network nodes and the m The interaction power of each IES; for Time-of-use distribution network nodes The load power; , These are the upper and lower limits of the voltage.

6. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 5, characterized in that, Step 5 includes: Step 5-1: Construct the objective function of IES using equations (24)-(27). : (24) (25) In equations (24)-(25): The objective function for the day-ahead execution of IES; For the IES Adjusting the objective function in a discrete scenario; Let IES be the energy purchase cost function; For device maintenance functions in IES; For the wind and solar power curtailment penalty function of IES; For the first Energy purchase cost adjustment function for IES in discrete scenarios; For the first Equipment maintenance cost adjustment function for IES in discrete scenarios; For the first Adjustment functions for wind and solar curtailment penalties in discrete scenarios of IES; For the first Carbon trading cost function of IES in discrete scenarios; , For the interaction characteristics of electricity and natural gas; for Power interaction between the distribution network and the m-th IES during the time period; for The amount of natural gas received from the upper-level gas network in the m-th IES of the time period; for Natural gas production from P2G equipment in the m-th IES during time period; D represents the set of all equipment in the IES. For equipment Unit operation and maintenance characteristic quantity; for Time-of-use equipment The output power; This refers to a collection of new energy generating units, including: photovoltaic and wind turbines; For the m-th new energy unit in the IES Maximum active power output; for New energy units in the m-th IES period The actual active power output; For the first In a discrete scenario The interaction power between the m-th IES and the distribution network adjustment in time period m; For the first In a discrete scenario The amount of adjusted natural gas received in the m-th IES during time period; For the first In a discrete scenario The amount of natural gas synthesized by the P2G in the power-to-gas conversion unit during the m-th time period of the IES is adjusted. For the first In a discrete scenario Time-of-use equipment Adjusted output power; For the first In a discrete scenario New energy units in the m-th IES period The actual adjusted active power output; For the first The actual carbon emissions of the m-th IES in a discrete scenario; For the first The carbon emission allowances for the m-th IES under discrete scenarios, where K represents the total number of discrete scenarios, and we have: (26) (27) In equations (26)-(27): for Time period m The node carbon potential of each IES; , for Time period m Carbon emissions of CHP cogeneration units and GB gas-fired boilers in each IES; for Time period m Carbon capture per IES; Step 5-2: Construct constraint models for the devices in the IES, including: the carbon capture system (CCS), the power-to-gas (P2G) device, the combined heat and power (CHP) unit, the gas boiler (GB), and the energy storage device; where CHP includes: gas turbine (GT) and waste heat boiler (WHB); and the energy storage device includes: battery (BT) and thermal storage tank (HST).

7. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 6, characterized in that, Step 5-2 includes: Step 5-2-1: Construct using equations (28) and (31) Constraint model of time period CCS: (28) (29) (30) (31) In equations (28)-(31): Carbon emission intensity of CCS; This refers to the calorific value of natural gas. for Time period m The amount of natural gas consumed by CCS in each IES; for Time period m Fixed energy consumption of CCS in each IES; For the first m Energy consumption of CCS in each IES; The energy required for CCS to process one unit of carbon dioxide; Step 5-2-2: Construct using equations (31) and (32) The operational model of time-based P2G: (32) (33) In equations (31)-(32): The heat energy that can be converted from a unit of electrical energy; For P2G working efficiency; for Time period m The electrical energy consumed by P2G in each IES; for Time period m The amount of carbon dioxide consumed by P2G in each IES; The amount of carbon dioxide required to generate one unit of natural gas; Step 5-2-3: Construct using equations (31) and (32) Constraint model for CHP units during specific time periods: (34) (35) In equations (34)-(35): , , Three carbon emission coefficients for gas-fired equipment; for Power generation of CHP during the period; for Heating power of CHP during the specified time period; The power generation efficiency of CHP; for Natural gas consumed by CHP during the period; for Waste heat from exhaust gas during the CHP period; This refers to the heat loss rate; For waste heat recovery efficiency; The flue gas recovery rate of CHP; Step 5-2-4: Construct using equations (36) and (37) Constraint model for GB-rated gas-fired boilers; (36) (37) In equations (36)-(37): for Heating power of GB during the time period; The heating efficiency is in GB. for Natural gas consumed during the GB period; Step 5-2-5: Construct using equations (38) and (39) Constraint model for time-of-use energy storage devices: (38) (39) In equations (38)-(39): for The state of charge of BT during the time period express The state of charge of BT during the time period; , for The charging and discharging power of BT during the time period; , for The charging and discharging efficiency of BT during the time period; for Thermal energy stored in the HST period; for Thermal energy stored in the HST period; , for The heat storage and release power of the HST period; The energy loss rate of HST; , The heat storage and release efficiency of HST.

8. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch according to claim 7, characterized in that, Step 6 includes: Step 6-1: Construct the first equation using equation (40). Penalty function of the IES running model after decoupling under the inner loop : (40) In equation (40): , for The multipliers of the linear and quadratic terms of the Lagrange penalty function corresponding to the m-th IES in time period; For the first Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first -1 inner loop Time period distribution network expectation and the first m The interaction power of each IES; Step 6-2: Construct the first equation using equation (41). Penalty function of the decoupled distribution network operation model under the second inner loop : (41) In equation (41), For the first -1 inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first Under the second inner loop Time period distribution network expectation and the first m The interaction power of each IES; Step 6-3: Construct the first equation using equation (42). Decoupling constraints under the inner loop: (42) In equation (42): for Time period m The interaction power value between each IES and the distribution network; This represents the maximum value of the interaction power between the IES and the distribution network.

9. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch as described in claim 8, characterized in that, Step 8 includes the following steps: Step 7-1: Construct a two-layer master model using equations (45) and (46): (45) (46) In equations (45)-(46): The IES objective function for the two-level master model; The objective function for the distribution network in the two-layer master model; The IES constraint objective for the two-layer sub-model; The distribution network constraint objective for the two-layer sub-model; The coefficients of the objective function; Forecast power output for new energy sources; For the first The output value of new energy sources in a discrete scenario; For the first The probability values ​​of a discrete scenario; Step 7-2: Construct a two-layer sub-model using equations (47) and (48): (47) (48) In equations (47)-(48), The IES objective function for the two-layer sub-model; The objective function for the distribution network in the two-layer sub-model; Let be the feasible region of the probability constraint; and we have: (49) In equation (49): Initial probability values ​​for scenarios that contribute power to new energy sources; , These are the permissible deviations of the probability under the constraints of the 1-norm and ∞-norm, respectively.

10. The hierarchical distributed robust operation method for distribution network-integrated energy system oriented towards low-carbon dispatch according to claim 9, characterized in that, Step 8 includes the following steps: Step 8-1: Define the number of iterations for the outer loop of the CCG as... Define the number of loop iterations in the ATC inner loop as ;initialization =1; Initialize the first Upper bound of distribution network target under secondary external circulation , No. Lower bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop , No. Lower bound of IES target under secondary outer loop =0 and the Under the second outer loop Probability values ​​of discrete scenarios ;definition To improve the convergence accuracy of the outer loop; Step 8-2: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The IES objective function of a bi-level master model under a secondary inner loop =0; initialize the first The second outer loop Objective function of the distribution network in the double-layer master model under the secondary inner loop =0; Step 8-3: Calculate the first step according to equations (45)-(46). The first iteration under the outer loop The overall objective function of the bi-level master model under the inner loop. IES objective function and its first The second outer loop Under the second inner loop The expected interaction power between the m-th IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period Thus, the first The second outer loop IES Action Set in Sub-Local Loop Including: the The second outer loop Predicted actions of each device in the IES during the second inner loop, as well as the interaction between the distribution network and the IES; Step 8-4: Determine whether equations (50) and (51) are valid. If they are valid, then let the first equation be valid. Lower bound of distribution network target under secondary external circulation , No. Lower bound of IES target under secondary outer loop Then, proceed to step 8-5; otherwise, Assign to After updating the penalty function according to equation (52), return to step 8-3 for sequential execution; (50) (51) (52) In equations (52)-(54), This represents the precision value for convergence condition 1 of the ATC algorithm; This represents the precision value for convergence condition 2 of the ATC algorithm; For the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in time period t under the inner loop; For the first The first iteration under the outer loop Under the second inner loop The expected interaction power between the IES and the distribution network during the m-th time period; For the first The first iteration under the outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period; Step 8-5: Initialization =1, initialize the first The second outer loop The linear term of the Lagrange penalty function corresponding to the m-th IES in the second inner loop period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop The quadratic term of the Lagrange penalty function corresponding to the m-th IES in the inner loop time period t. For fixed values ​​between (0, 0.1); initialize the first... The second outer loop IES objective function of the two-level sub-model under the inner loop =0; initialize the first The second outer loop Objective function of distribution network in the two-layer sub-model under the inner loop =0; Step 8-6: Based on Calculate the first one according to equations (53) and (54). Under the second outer loop Probability values ​​of discrete scenarios , No. The first iteration under the outer loop The overall objective function of the bi-level sub-model under the inner loop. and its first The first iteration under the outer loop Under the second inner loop The expected interaction power between the m-th IES and the distribution network Overall objective function of the distribution network in the two-level master model and its first The second outer loop Under the second inner loop The expected interaction power between the distribution network and the m-th IES during the time period Thus, the first The second outer loop IES Adjustment Action Set under Secondary Loop and the The first iteration under the outer loop Distribution network action scheme set under secondary cycle ,in, Including: the The second outer loop The adjustment actions of each device in the IES during the secondary loop, as well as the interactive adjustment actions between the distribution network and the IES. Including the The second outer loop R-SOP action in the next internal loop; (53) (54) In equations (53) and (54), For the first The second outer loop In the second inner loop, the IES in the bilayer sub-model is at the... k The optimal objective function for a discrete scenario; For the first The second outer loop The distribution network in the two-layer sub-model under the second inner cycle is in the... k The optimal objective function for a discrete scenario; For the first k A set of new energy power outputs in discrete scenarios; Step 8-7: Determine whether equations (55) and (56) are valid. If they are valid, obtain the first... Upper bound of distribution network target under secondary external circulation , No. Upper bound of IES target under secondary outer loop Then, proceed to step 8-8; otherwise, Assign to Then, update the penalty function according to equation (52) and return to steps 8-6 to execute sequentially; (55) (56) Step 8-8: Judgment as well as If the condition is met, then the final operating plan has been obtained. Otherwise, Assign to Then, return to step 8-2 and execute sequentially.

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