A distributed energy interaction method for district energy internet and power distribution network

Abstract

The present application relates to a kind of district energy internet and distribution network distributed energy interaction method;First, the distribution network, district energy internet energy interaction optimization model is established respectively;On this basis, the distributed energy interaction solving method of distribution network and multiple district energy internet based on alternate direction multiplier method is proposed;The energy interaction method of the present application not only overcomes the defect that centralized optimization algorithm is difficult to protect the privacy of each interaction subject, but also simultaneously considers the coordinated interaction of active power and reactive power, realizes the flexible interaction of district energy internet and distribution network and distributed operation optimization.

Description

Technical Field

[0001] This invention relates to a method for interaction between regional energy internet and distributed energy in distribution networks, belonging to the field of distribution network energy management technology. Background Technology

[0002] With the vigorous construction and development of regional energy internet, the regional energy internet and distribution network are showing a trend of increasingly deep integration and interaction. Regional energy internet integrates multiple resources such as wind, solar, thermal, and natural gas, and can be viewed as a multi-energy producer-consumer. Therefore, the interaction between regional energy internet and distribution network is bidirectional; it can purchase electricity from the distribution network to meet local demand and sell surplus electricity back to the distribution network to increase revenue. Furthermore, regional energy internet can achieve multi-energy complementarity and energy substitution, possessing greater interaction potential. Distributed power sources also have a certain reactive power compensation capability. Therefore, the interaction between regional energy internet and distribution network is multi-faceted, deeply integrated, and bidirectional. How to construct an energy interaction optimization model between regional energy internet and distribution network to achieve flexible and in-depth interaction is one of the urgent problems to be solved in the field of distribution network energy management, dispatching, and operation.

[0003] Current research has focused on the interactive operation of regional energy internet and distribution networks. For example, Chinese invention patent CN112598224A, "An Interactive Game-Based Scheduling Method for Integrated Energy System Groups in Industrial Parks and Distribution Networks," discloses a master-slave game-based interactive scheduling method between integrated energy system groups in industrial parks and the power grid, achieving active power interaction between the two. Another example is Chinese invention patent CN113393126A, "An Alternating Parallel Cooperative Optimization Scheduling Method for High-Energy-Consuming Industrial Parks and Power Grids," which discloses an alternating cooperative optimization scheduling method for high-energy-consuming industrial parks and the power grid, utilizing the alternating direction multiplier method for distributed solution. However, most existing methods do not consider the operational constraints of the distribution network and the coordinated interaction of reactive power, making it difficult to achieve multi-faceted and in-depth interaction between the regional energy internet and the distribution network. Furthermore, many methods employ centralized optimization for solution, which is detrimental to protecting the privacy and security of participating entities. Therefore, how to simultaneously achieve active and reactive power interaction between the regional energy internet and the distribution network, and how to achieve distributed coordinated interaction between the regional energy internet and the distribution network, are crucial issues that urgently need to be addressed in the field of distribution network energy management technology. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a distributed energy interaction method between regional energy internet and distribution network, establishing energy interaction optimization models for regional energy internet and distribution network respectively. Based on this, a distributed energy interaction method for multiple regional energy internet and distribution network based on the alternating direction multiplier method is proposed. The energy interaction method of this invention overcomes the shortcomings of centralized optimization algorithms in protecting the privacy of each interaction subject and in simultaneously taking into account the coordinated interaction of active and reactive power, realizing flexible interaction and distributed operation optimization between regional energy internet and distribution network.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for interaction between regional energy internet and distributed energy in distribution networks includes the following steps:

[0007] Step 1: With the goal of minimizing operating costs, and taking into account the operating constraints of the distribution network, establish an energy interaction optimization model for the interaction between the distribution network and the regional energy internet;

[0008] Step 2: With the goal of minimizing operating costs, and taking into account the operational constraints of the regional energy internet equipment, establish an energy interaction optimization model for the interaction between the regional energy internet m and the distribution network; the regional energy internet equipment includes energy conversion equipment and energy storage equipment.

[0009] Step 3: Based on the energy interaction optimization model of the distribution network and regional energy internet established in Step 1 and Step 2, establish a solution method for distributed energy interaction between the regional energy internet and the distribution network;

[0010] Further, in step 1 above, an energy interaction optimization model between the distribution network and the regional energy internet is established: the operation objective of the distribution network is to minimize the comprehensive operating cost, and the objective function can be represented by equation (1), which consists of 7 terms. The first term is the network loss cost of the distribution network, the second term is the electricity sales revenue of the distribution network to each regional energy internet, the third term is the reactive power regulation cost paid by the distribution network to each regional energy internet, and the fourth to seventh terms are the energy interaction cost; the operation constraints are represented by equation (2), the first and second equations represent the active and reactive power balance constraints of the nodes, the third equation represents the branch voltage constraint, the fourth equation represents the branch power flow constraint, and the fifth and sixth equations represent the node voltage constraint and the branch current constraint, respectively; the establishment of the energy interaction optimization model of the distribution network specifically includes the following steps:

[0011] Step (1-1): Determine the objective function of the energy interaction optimization model of the distribution network according to equation (1):

[0012]

[0013] In the formula, λ lossHere, B represents the network loss cost coefficient, expressed in yuan / kW·h; t∈{1,2,…,T} is the time period set; and B is the branch set. r ij Let be the resistance of branch ij. Ω represents the square of the current in branch ij at time t; m is the index of the regional energy internet. The electricity price sold or repurchased by the distribution network at time t is expressed in yuan / kW·h. The compensation price for the regional energy internet's participation in reactive power regulation of the distribution network at time t is yuan / kVar; This represents the expected interaction power between the distribution network and the m-th regional energy internet at time t. This represents the active power expected to interact with the distribution network in the regional energy internet at time t. This represents the reactive power that the distribution network expects to interact with the regional energy internet m. This represents the reactive power that the regional energy internet m expects to interact with the distribution network. and ρ is a Lagrange multiplier, and ρ is the penalty coefficient.

[0014] Step (1-2): Determine the operating constraints of the energy interaction optimization model of the distribution network according to the following equation (2):

[0015]

[0016] In the formula, N is the set of distribution network nodes, and B is the set of branches. Let represent the square of the current in branch ij at time t. Let represent the square of the voltage at node i at time t. and These represent the maximum and minimum squared voltage values, respectively. and These represent the maximum and minimum squared current values, respectively; Ω represents the set of regional energy internet networks, and m represents the index of the regional energy internet network. j For the set of regional energy internet nodes accessing node j; Let represent the active power of the m-th regional energy internet interacting with node j at time t, and let represent the expected interaction power between the distribution network and the m-th regional energy internet at time t. A positive value indicates the expectation to sell electricity to the regional energy internet, and a negative value indicates the expectation to purchase electricity from the regional energy internet. This represents the basic active load of node j; This represents the reactive power injected into node j by the regional energy internet m. A positive value indicates that the regional energy internet m is expected to provide reactive power, while a negative value indicates that the regional energy internet m is expected to absorb reactive power. This represents the reactive power injected into node j by the reactive power compensator; Let represent the basic reactive load of node j; δ(j) represent the set of terminal nodes of the branch with node j as the starting node; φ(j) represent the set of starting nodes of the branch with node j as the ending node; r ij and x ij ... and These represent the active and reactive power flowing from node j into branch k during time period t; and These represent the active and reactive power of branch ij during time period t;

[0017] Furthermore, step 2 above specifically includes the following steps:

[0018] Step (2-1): Determine the energy interaction objective function of the regional energy internet m according to equation (3):

[0019]

[0020] in, The operating cost of the regional energy internet m; This represents the amount of natural gas purchased by the regional energy internet at time t. and Let represent the expected active and reactive power of the distribution network interacting with the m-th regional energy internet at time t, respectively; This represents the active power that the regional energy internet m expects to interact with the distribution network at time t. A positive value indicates that the regional energy internet expects to purchase electricity from the distribution network, while a negative value indicates that it expects to sell electricity to the distribution network. This represents the reactive power that the regional energy internet m expects to interact with the distribution network. A positive value indicates that the reactive power expected to be provided to the distribution network, while a negative value indicates that the reactive power expected to be absorbed from the distribution network. and ρ is the Lagrange multiplier; ρ is the penalty coefficient;

[0021] Step (2-2): Determine the energy balance constraints of the regional energy internet m according to equation (4):

[0022]

[0023] In equation (4), the first equation represents the electrical power balance, the second equation represents the thermal power balance, and the third equation represents the natural gas power balance. Let m be the photovoltaic power generation capacity of the regional energy internet at time t. Let be the electrical load at time t; and These represent the charging and discharging power of the stored energy at time t, respectively. and These represent the natural gas input power of the gas turbine and the gas boiler at time t, respectively. and These are the electrical efficiency and thermal efficiency of the gas turbine, respectively. For gas-fired boiler efficiency; and These represent the heat storage and heat release power of the thermal storage device at time t;

[0024] Step (2-3): Determine the operating constraints of gas turbines, gas boilers, electric energy storage, and thermal energy storage equipment in the regional energy internet m according to equations (5), (6), and (7):

[0025]

[0026] in and These are the minimum and maximum interactive power constraints for the regional energy internet, respectively. and These are the minimum and maximum output power constraints for the gas turbine, respectively. and These are the minimum and maximum output power constraints for the gas-fired boiler, respectively.

[0027]

[0028] in This represents the stored energy of electrical energy at time t. The energy loss rate of electrical energy storage and These represent the charging and discharging efficiencies of electrical energy storage, respectively. and These are the maximum charging and discharging power of the electrical energy storage, respectively. It is a binary variable. and These represent the minimum and maximum energy storage capacity of electrical energy storage, with the last term indicating that the energy storage capacity is equal at the beginning and end of the operating cycle.

[0029]

[0030] in This represents the amount of heat stored at time t. The heat loss rate of thermal energy storage. and These represent the heat storage efficiency and the heat release efficiency, respectively. and These are the maximum heat charging and heat releasing power of thermal energy storage, respectively. It is a binary variable. and These represent the minimum and maximum heat storage capacity of thermal energy storage, with the last term indicating that the heat storage capacity is equal at the beginning and end of the operating cycle.

[0031] Furthermore, the specific steps of step 3 are as follows:

[0032] Step (3-1): Initialize the maximum number of iterations k for the distributed energy interaction alternating iterative algorithm. max Iterative convergence accuracy Initialize Lagrange multipliers and Initialize the penalty coefficient ρ and the iteration number k; initialize the expected active power of the energy internet in each region to interact with the distribution network. and reactive power

[0033] Step (3-2): The distribution network receives the active power for desired interaction from the regional energy internet. and reactive power Solving equations (1)-(2), we obtain the active power expected to interact between the distribution network and the regional energy internet m. and reactive power

[0034] Step (3-3): For the regional energy internet m, receive the active power from the distribution network for the desired interaction with the distribution network. and reactive power Solving equations (3)-(7) yields the expected active power of its interaction with the distribution network. and reactive power

[0035] Step (3-4): Update the Lagrange multipliers according to equation (8):

[0036]

[0037] Step (3-5): Update the number of iterations: k = k + 1;

[0038] Step (3-6): Determine the convergence status of the algorithm. If the iteration termination condition (9) is met:

[0039]

[0040] If the iteration terminates, the process returns to step (3-2) and the calculation is repeated until the convergence condition or the maximum number of iterations is met.

[0041] The advantages of this invention compared to the prior art are:

[0042] (1) This invention simultaneously realizes the interaction of active and reactive power between the power distribution network and the regional energy internet;

[0043] (2) This invention uses the alternating direction multiplier method to realize the distributed energy interaction solution of the distribution network and multiple regional energy internet, and realizes the distributed coordinated operation control of the regional energy internet;

[0044] (3) The distributed energy interaction method proposed in this invention can realize the distributed solution optimization of the operation strategy of distribution network and regional energy Internet, and protect the privacy information security of each participating entity.

[0045] In summary, the interaction method of this invention can simultaneously realize the interaction of active and reactive power between the regional energy internet and the distribution network, overcoming the shortcomings of existing technologies that are mostly used only for active power interaction. The proposed distributed energy interaction method between the distribution network and multiple regional energy internets based on the alternating direction multiplier method overcomes the shortcomings of centralized optimization algorithms in protecting the privacy of each interactive entity, and realizes flexible interaction and distributed operation optimization between the regional energy internet and the distribution network. Attached Figure Description

[0046] Figure 1 This is a framework diagram of the regional energy internet and distributed energy interaction method of the distribution network of the present invention;

[0047] Figure 2 This is a schematic diagram of the regional energy internet system of the present invention;

[0048] Figure 3 This is an overall flowchart of the regional energy internet and distributed energy interaction method of the distribution network of the present invention;

[0049] Figure 4 This is a flowchart of the distributed energy interaction solution method for regional energy internet and distribution network of the present invention. Detailed Implementation

[0050] The present invention will now be described in detail with reference to the accompanying drawings and implementation process.

[0051] like Figure 1 As shown in the framework diagram of the regional energy internet and the distributed energy interaction method of the distribution network of the present invention, the distribution network and each regional energy internet are different operating entities. The regional energy internet operates in parallel with the distribution network through different nodes and interacts with the distribution network in terms of active and reactive energy, ultimately realizing the distributed optimized operation of the entire system.

[0052] like Figure 2The schematic diagram of the regional energy internet system is shown. The regional energy internet system includes photovoltaics, gas turbines, gas boilers, electric energy storage, and thermal energy storage. The regional energy internet is connected to the distribution network and the natural gas network. By participating in the interaction of active and reactive energy in the distribution network, it continuously optimizes its own operation strategy and maximizes its own benefits while meeting the needs of terminal electrical and thermal loads.

[0053] This invention discloses a distributed energy interaction method between a regional energy internet and a distribution network. First, energy interaction optimization models for the regional energy internet and the distribution network are established respectively. Based on these models, a distributed energy interaction method for multiple regional energy internet and distribution networks based on the alternating direction multiplier method is proposed. This distributed energy interaction method not only overcomes the shortcomings of centralized optimization algorithms in protecting the privacy of each interacting entity, but also simultaneously considers the coordinated interaction of active and reactive power, realizing flexible interaction and distributed operation optimization between the regional energy internet and the distribution network. Specifically, the distributed energy interaction method includes the following steps:

[0054] Step 1: With the goal of minimizing operating costs, and taking into account the operating constraints of the distribution network, establish an energy interaction optimization model for the interaction between the distribution network and the regional energy internet.

[0055] The operational objective of the distribution network is to minimize operating costs. The objective function can be represented by equation (1), which consists of seven terms: the first term is the network loss cost of the distribution network; the second term is the electricity sales revenue of the distribution network to various regional energy internets; the third term is the reactive power regulation cost paid by the distribution network to various regional energy internets; and the fourth to seventh terms are energy interaction costs. Operational constraints are represented by equation (2), where the first two equations represent the active power balance constraint and reactive power balance constraint at nodes, the third equation represents the branch voltage constraint, the fourth equation represents the branch power flow constraint, and the fifth and sixth equations represent the node voltage constraint and the branch current constraint, respectively. Establishing the energy interaction optimization model of the distribution network specifically includes the following steps:

[0056] Step (1-1): Determine the objective function of the energy interaction optimization model of the distribution network according to equation (1):

[0057]

[0058] In the formula, λ loss Here, B represents the network loss cost coefficient, expressed in yuan / kW·h; t∈{1,2,…,T} is the time period set; and B is the branch set. r ij Let be the resistance of branch ij. Ω represents the square of the current in branch ij at time t; m is the index of the regional energy internet. The electricity price sold or repurchased by the distribution network at time t is expressed in yuan / kW·h. The compensation price for the regional energy internet's participation in reactive power regulation of the distribution network at time t is yuan / kVar; This represents the expected interaction power between the distribution network and the m-th regional energy internet at time t. This represents the active power expected to interact with the distribution network in the regional energy internet at time t. This represents the reactive power that the distribution network expects to interact with the regional energy internet m. This represents the reactive power that the regional energy internet m expects to interact with the distribution network. and ρ is a Lagrange multiplier, and ρ is a penalty coefficient.

[0059] Step (1-2): Determine the operating constraints of the energy interaction optimization model of the distribution network according to equation (2):

[0060]

[0061] In the formula, N is the set of distribution network nodes, and B is the set of branches. Let represent the square of the current in branch ij at time t. Let represent the square of the voltage at node i at time t. and These represent the maximum and minimum squared voltage values, respectively. and These represent the maximum and minimum squared current values, respectively; Ω represents the set of regional energy internet networks, and m represents the index of the regional energy internet network. j For the set of regional energy internet nodes accessing node j; Let represent the active power of the m-th regional energy internet interacting with node j at time t, and let represent the expected interaction power between the distribution network and the m-th regional energy internet at time t. A positive value indicates the expectation to sell electricity to the regional energy internet, and a negative value indicates the expectation to purchase electricity from the regional energy internet. This represents the basic active load of node j; This represents the reactive power injected into node j by the regional energy internet m. A positive value indicates that the regional energy internet m is expected to provide reactive power, while a negative value indicates that the regional energy internet m is expected to absorb reactive power. This represents the reactive power injected into node j by the reactive power compensator; Let represent the basic reactive load of node j; δ(j) represent the set of terminal nodes of the branch with node j as the starting node; φ(j) represent the set of starting nodes of the branch with node j as the ending node; r ij and x ij ... and These represent the active and reactive power flowing from node j into branch k during time period t; and These represent the active and reactive power of branch ij during time period t;

[0062] Step 2: With the goal of minimizing operating costs, and taking into account the operational constraints of energy conversion and storage devices in the regional energy internet, establish an energy interaction optimization model for the interaction between the regional energy internet m and the distribution network. This specifically includes the following steps:

[0063] Step (2-1): Determine the energy interaction objective function of the regional energy internet m according to equation (3):

[0064]

[0065] in, The operating cost of the regional energy internet m; This represents the amount of natural gas purchased by the regional energy internet at time t. and Let represent the expected active and reactive power of the distribution network interacting with the m-th regional energy internet at time t, respectively; This represents the active power that the regional energy internet m expects to interact with the distribution network at time t. A positive value indicates that the regional energy internet expects to purchase electricity from the distribution network, while a negative value indicates that it expects to sell electricity to the distribution network. This represents the reactive power that the regional energy internet m expects to interact with the distribution network. A positive value indicates that the reactive power expected to be provided to the distribution network, while a negative value indicates that the reactive power expected to be absorbed from the distribution network. and ρ is the Lagrange multiplier; ρ is the penalty coefficient;

[0066] Step (2-2): Determine the energy balance constraints of the regional energy internet m according to equation (4):

[0067]

[0068] In equation (4), the first equation represents the electrical power balance, the second equation represents the thermal power balance, and the third equation represents the natural gas power balance. Let m be the photovoltaic power generation capacity of the regional energy internet at time t. Let be the electrical load at time t; and These represent the charging and discharging power of the stored energy at time t, respectively. and These represent the natural gas input power of the gas turbine and the gas boiler at time t, respectively. and These are the electrical efficiency and thermal efficiency of the gas turbine, respectively. For gas-fired boiler efficiency; and These represent the heat storage and heat release power of the thermal storage device at time t;

[0069] Step (2-3): Determine the operating constraints of the gas turbine, gas boiler, electric energy storage equipment, and thermal energy storage equipment in the regional energy internet m according to equations (5), (6), and (7):

[0070]

[0071] in and These are the minimum and maximum interactive power constraints for the regional energy internet, respectively. and These are the minimum and maximum output power constraints for the gas turbine, respectively. and These are the minimum and maximum output power constraints for the gas-fired boiler, respectively.

[0072]

[0073] in This represents the stored energy of electrical energy at time t. The energy loss rate of electrical energy storage and These represent the charging and discharging efficiencies of electrical energy storage, respectively. and These are the maximum charging and discharging power of the electrical energy storage, respectively. It is a binary variable. and These represent the minimum and maximum energy storage capacity of electrical energy storage, with the last term indicating that the energy storage capacity is equal at the beginning and end of the operating cycle.

[0074]

[0075] in This represents the amount of heat stored at time t. The heat loss rate of thermal energy storage. and These represent the heat storage efficiency and the heat release efficiency, respectively. and These are the maximum heat charging and heat releasing power of thermal energy storage, respectively. It is a binary variable. and These represent the minimum and maximum heat storage capacity of thermal energy storage, with the last term indicating that the heat storage capacity is equal at the beginning and end of the operating cycle.

[0076] Step 3: Based on the energy interaction optimization model of the distribution network and regional energy internet established in Steps 1 and 2, establish a solution method for distributed energy interaction between the regional energy internet and the distribution network, such as... Figure 4 As shown, the specific steps are as follows:

[0077] Step (3-1): Initialize the maximum number of iterations k for the distributed energy interaction alternating iterative algorithm. max Iterative convergence accuracy Initialize Lagrange multipliers and Initialize the penalty coefficient ρ and the iteration number k; initialize the expected active power of the energy internet in each region to interact with the distribution network. and reactive power

[0078] Step (3-2): The distribution network receives the active power for desired interaction from the regional energy internet. and reactive power Solving equations (1)-(2), we obtain the active power expected to interact between the distribution network and the regional energy internet m. and reactive power

[0079] Step (3-3): For the regional energy internet m, receive the active power from the distribution network for the desired interaction with the distribution network. and reactive power Solving equations (3)-(7) yields the expected active power of its interaction with the distribution network. and reactive power

[0080] Step (3-4): Update the Lagrange multipliers according to equation (8):

[0081]

[0082] Step (3-5): Update the number of iterations: k = k + 1;

[0083] Step (3-6): Determine the convergence of the algorithm. If the iteration termination condition of equation (9) is satisfied:

[0084]

[0085] If the iteration terminates, the process returns to step (3-2) and the calculation is repeated until the convergence condition or the maximum number of iterations is met.

[0086] The above implementation steps are provided merely for the purpose of describing the present invention and are not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims. All equivalent substitutions and modifications made without departing from the spirit and principles of the invention should be covered within the scope of the invention.

Claims

1. A method for interaction between regional energy internet and distributed energy in distribution networks, characterized in that, Includes the following steps: Step 1: With the goal of minimizing operating costs, and taking into account the operational constraints of the distribution network, establish an energy interaction optimization model for the interaction between the distribution network and the regional energy internet. This model includes the following steps: The operational objective of the distribution network is to minimize the overall operating cost. The objective function is represented by equation (1), which consists of seven terms: the first term is the network loss cost of the distribution network, the second term is the revenue from the distribution network's electricity sales to various regional energy internets, the third term is the reactive power regulation cost paid by the distribution network to various regional energy internets, and the fourth to seventh terms are the energy interaction costs. The operational constraints are represented by equation (2): the first and second equations represent the active and reactive power balance constraints at nodes, the third equation represents the branch voltage constraints, the fourth equation represents the branch power flow constraints, and the fifth and sixth equations represent the node voltage constraints and branch current constraints, respectively. The specific steps for establishing the energy interaction optimization model of the distribution network are as follows: Step (1-1): Determine the objective function of the energy interaction optimization model of the distribution network according to the following equation (1): (1) In the formula, The network loss cost coefficient is expressed in yuan / kW·h. A set of time periods; For branch road collection, , , branch road The resistance, express Time Branch The square of the current; For the regional energy internet aggregation, For regional energy internet indexing; for The electricity sales or buyback price on the power distribution network at any time, in yuan / kW·h; for The compensation price for reactive power regulation of the distribution network by the regional energy internet at any given time, in yuan / kVar; express The expected value of the distribution network at any time and the first Interactive power of regional energy internet express Time-based regional energy internet The active power expected to interact with the distribution network; This indicates the expectations of the distribution network and the regional energy internet. Interactive reactive power, Indicating regional energy internet Reactive power expected to interact with the distribution network; and For Lagrange multipliers, This is the penalty coefficient; Step (1-2): Determine the operating constraints of the energy interaction optimization model of the distribution network according to the following equation (2): (2) In the formula, For the set of distribution network nodes, For branch road collection, , ; express Time Branch The square of the current, Represents the node at time t The square of the voltage, and These represent the maximum and minimum squared voltage values, respectively. and These represent the square values ​​of the maximum and minimum currents, respectively. For the regional energy internet aggregation, For regional energy internet indexing, For access nodes A collection of regional energy internet; Indicates the first Regional energy internet in Time and Node The active power of the interaction, representing The expected value of the distribution network at any time and the first The regional energy internet interaction power is positive, indicating the expectation to sell electricity to the regional energy internet, and negative, indicating the expectation to purchase electricity from the regional energy internet. Represents a node The basic active load; Indicating regional energy internet Injection Node The reactive power is positive, representing the expected regional energy internet. Provides reactive power; negative values ​​indicate the desired regional energy internet. Absorbing reactive power; Indicates the reactive power compensator injection node reactive power; Represents a node Basic reactive load; Indicates It is the set of branch end nodes of the first node; Indicates The set of the starting nodes of the branches of the terminal nodes; and Branch roads The resistance and reactance values; It was thought This is the branch index for the first node. and They are respectively Time period is determined by nodes Inflow branch The active and reactive power; and They are respectively Time-of-day branch Active and reactive power; Step 2: With the goal of minimizing operating costs, and taking into account the operational constraints of regional energy internet equipment, establish a regional energy internet. An energy interaction optimization model that interacts with the power distribution network; the regional energy internet equipment includes energy conversion equipment and energy storage equipment. Step 3: Based on the energy interaction optimization model of the distribution network and regional energy internet established in Step 1 and Step 2, establish a solution method for distributed energy interaction between the regional energy internet and the distribution network.

2. The method for interaction between regional energy internet and distributed energy in distribution network according to claim 1, characterized in that: Step 2 specifically includes the following steps: Step (2-1): Determine the regional energy internet according to equation (3). Energy interaction objective function: (3) in, For regional energy internet Operating costs; for The electricity sales or buyback price on the power distribution network at any time, in yuan / kW·h; express Real-time natural gas prices express Time-based regional energy internet Purchased natural gas; and They represent The expected value of the distribution network at any time and the first Active and reactive power of regional energy internet interaction; express Time-based regional energy internet The active power expected to interact with the distribution network, a positive value indicates that the regional energy internet expects to purchase electricity from the distribution network, and a negative value indicates that it expects to sell electricity to the distribution network; Indicating regional energy internet The reactive power expected to interact with the distribution network, a positive value indicates that reactive power is expected to be provided to the distribution network, and a negative value indicates that reactive power is expected to be absorbed from the distribution network. and For Lagrange multipliers; This is the penalty coefficient; Step (2-2): Determine the regional energy internet according to the following formula (4). Energy balance constraints: (4) In equation (4), the first equation represents the power balance, the second equation represents the power balance, and the third equation represents the power balance of natural gas. for Time-based regional energy internet Photovoltaic power generation capacity for Electrical load at any given time; express Time-based regional energy internet The active power expected to interact with the distribution network; and They are respectively The charging and discharging power of the electrical energy storage at all times; express Time-based regional energy internet Total natural gas consumption power; and They are respectively The power consumption of natural gas by gas turbines and gas boilers at all times; and These are the electrical efficiency and thermal efficiency of the gas turbine, respectively. For gas-fired boiler efficiency; and They are respectively The heat storage and heat release capacity of the thermal storage device at all times; express The heat load at any given time; Step (2-3): Determine the regional energy internet according to equations (5), (6) and (7) respectively. Operating constraints of gas turbines, gas boilers, electric energy storage, and thermal energy storage equipment: (5) in, and Regional Energy Internet Minimum and maximum interaction power constraint values; express Time-based regional energy internet The active power expected to interact with the distribution network; and These are the minimum and maximum output power constraints for the gas turbine, respectively. and They are respectively The power consumption of natural gas by gas turbines and gas boilers at all times; For the electrical efficiency of the gas turbine, For gas-fired boiler efficiency; and These are the minimum and maximum output power constraints for the gas-fired boiler, respectively. (6) in, express Energy storage that can be stored at any time. The energy loss rate of electrical energy storage and These represent the charging and discharging efficiencies of electrical energy storage, respectively. and These are the maximum charging and discharging power of the electrical energy storage, respectively. It is a binary variable. and These represent the minimum and maximum energy storage capacity of electrical energy storage, with the last term indicating that the energy storage capacity is equal at the beginning and end of the operating cycle. (7) in, express The amount of heat stored in thermal energy storage at all times. The heat loss rate of thermal energy storage. and These represent the heat storage efficiency and the heat release efficiency, respectively. and These are the maximum heat charging and heat releasing power of thermal energy storage, respectively. It is a binary variable. and These represent the minimum and maximum heat storage capacity of thermal energy storage, with the last term indicating that the heat storage capacity is equal at the beginning and end of the operating cycle.

3. The method for interaction between regional energy internet and distributed energy in distribution network according to claim 2, characterized in that: The specific steps of step 3 are as follows: Step (3-1): Initialize the maximum number of iterations for the distributed energy interaction alternation iterative algorithm Number of iterations and iterative convergence accuracy Initialize Lagrange multipliers and Initialize the penalty coefficient ; Initialize the active power expected to interact with the distribution network in each regional energy internet. and reactive power ; Step (3-2): The distribution network receives the active power for desired interaction from the regional energy internet. and reactive power Solving equations (1) and (2), we obtain the expected distribution network and regional energy internet. Interactive active power and reactive power ; Step (3-3): For the regional energy internet Receive active power from the distribution network for desired interaction with the distribution network. and reactive power Solving equations (3)-(7) yields the expected active power of its interaction with the distribution network. and reactive power ; Step (3-4): Update the Lagrange multipliers according to equation (8): (8) Step (3-5): Update the number of iterations: k = k + 1; Step (3-6): Determine the convergence of the algorithm. If the iteration termination condition (9) is met: (9) If the iteration terminates, the process returns to step (3-2) and the calculation is repeated until the convergence condition or the maximum number of iterations is met.

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