Distributed integrated energy microgrid group optimization scheduling method based on bidirectional auction P2P transaction mechanism

Abstract

The invention relates to the field of integrated energy micro-grid scheduling, and discloses a distributed integrated energy micro-grid group optimization scheduling method based on a bidirectional auction P2P transaction mechanism, and the method comprises the steps: enabling an integrated energy micro-grid group dispatcher to organize all integrated energy micro-grids under the jurisdiction to carry out P2P transaction; the transaction plan is submitted to a power distribution system dispatcher to check and return the transaction electric quantity transmission cost, the comprehensive energy microgrid group dispatcher organizes a new round of P2P transaction according to the new transmission cost, and finally economic dispatching of the dispersedly distributed comprehensive energy microgrid group is achieved. According to the method, the influence of P2P transactions among the integrated energy micro-grids on the network loss of the power distribution system and the limitation of the operation safety of the power distribution system on the transaction plan can be considered, and the overall economic operation of the dispersedly distributed integrated energy micro-grid group is realized.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy microgrid scheduling, and in particular to a decentralized integrated energy microgrid group optimization scheduling method based on a double auction P2P transaction mechanism. Background Art

[0002] Based on the needs of dual-carbon transformation, a large amount of distributed power generation of renewable energy such as wind and light is connected to the low-voltage distribution network relying on public buildings and factory spaces, and together with the cold and heat energy systems of buildings and factories, it constitutes a comprehensive energy microgrid. By coordinating distributed power generation, energy storage and flexible loads within the microgrid, it can effectively reduce the impact of fluctuations in renewable energy power generation, which is an effective way to improve my country's energy clean and low-carbon level.

[0003] On this basis, multiple integrated energy microgrids that are geographically close can establish a peer-to-peer transaction mechanism between microgrids with the help of regional distribution systems to further promote the local consumption of distributed renewable energy. To this end, it is necessary to build a dispatching method that takes into account the safety and economy of the integrated energy microgrid, the integrated energy microgrid cluster, and the regional distribution system. Summary of the invention

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] The distributed integrated energy microgrid group optimization scheduling method based on the double auction P2P transaction mechanism includes the following steps:

[0006] Step 1: Set the scheduling time t;

[0007] Step 2: Each IEMO reads the parameters of power generation, heating / cooling, power / heat / cooling storage equipment, and electricity, heating, and cooling load data within the integrated energy microgrid under its jurisdiction;

[0008] Step 3: For the trading period t, each IEMO optimizes the scheduling of its own integrated energy microgrid and generates a power purchase and sales plan within the microgrid group based on the optimization results;

[0009] Step 4: For the integrated energy microgrid with electricity sales demand, after receiving the electricity purchase plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of maximizing electricity sales revenue, and the electricity sales transaction plan is calculated; For the integrated energy microgrid with electricity purchase demand, after receiving the electricity sales plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of minimizing electricity purchase cost, and the electricity purchase transaction plan is calculated;

[0010] Step 5: Each IEMO uploads its own trading plan to IEMCO for confirmation; IEMCO uploads the confirmed P2P trading plan between microgrids and the plan of purchasing or selling electricity to the distribution system to DSO;

[0011] Step 6: Based on the distribution user load and the transaction information of the microgrid group, the DSO uses the optimization dispatch model to solve the change in system network loss cost before and after the implementation of P2P transactions and the power reduction of each P2P transaction, and sends it to the microgrid group dispatcher IEMCO;

[0012] Step 7: IEMCO determines whether the power reduction of each P2P transaction is less than the preset value ε. If so, it jumps out of the loop and outputs the microgrid P2P transaction plan; otherwise, it calculates the transaction power limit, updates the P2P transaction service fee of each integrated energy microgrid, and goes to step 3.

[0013] Step 3: An integrated energy microgrid optimization dispatching model using P2P transactions;

[0014] Taking any dispatching time t as an example, under the P2P trading mode, the optimal dispatching objective function of the integrated energy microgrid i is shown in formula (1):

[0015]

[0016] In the formula, is the total power supply cost of integrated energy microgrid i at time t, is the dispatching cost within the integrated energy microgrid i, The cost of purchasing electricity from the distribution system or the revenue of selling electricity for the integrated energy microgrid i, is the expected revenue of P2P transactions of integrated energy microgrid i;

[0017] The cost calculation method of each item in formula (1) is as follows:

[0018]

[0019] In the formula is the power generation cost of the internal combustion engine, is the power generation of the internal combustion engine, is the power generation cost of the CHP unit, is the power generation capacity of the CHP unit, is the operation and maintenance cost of the electric refrigerator, is the input power of the electric refrigerator, is the operation and maintenance cost of the energy storage equipment, are the energy release and storage power of the energy storage device, Ω MT ,Ω CHP ,Ω EC ,Ω ES They are respectively a collection of internal combustion engines, combined heat and power units, electric refrigerators, and energy storage devices; c DS According to the transaction status between the integrated energy microgrid and the distribution system, the electricity sales price or electricity purchase price of the distribution system is selected. Trading electric power for integrated energy microgrids and distribution systems, and They are the market transaction electricity price between integrated energy microgrids and the service fee charged by the distribution system. It is the trading power between integrated energy microgrids. It is calculated from the basic service fee and the network loss fee caused by P2P transactions. It takes a positive value for the electricity seller and a negative value for the electricity buyer. Δt is the time interval of the dispatch cycle.

[0020] The optimal dispatch of the integrated energy microgrid i needs to meet the following power balance constraints, including the electric bus balance constraint, the thermal bus balance constraint, and the cold bus balance constraint:

[0021]

[0022] In the formula and are the discharge power and charging power of the energy storage device, is the power consumption in the integrated energy microgrid i, Ω EES A collection of electrical energy storage devices; and are the heat release power and heat storage power of the thermal energy storage equipment, is the thermal power in the integrated energy microgrid i, Ω HES A collection of thermal energy storage devices; and are the cooling power and cold storage power of the cold storage equipment, is the cooling power in the integrated energy microgrid i, Ω EC A collection of electric refrigerators; is the refrigeration coefficient of the electric refrigerator, is the input power of the ice storage device (ice storage air conditioner), Ω CES A collection of ice storage air conditioners;

[0023] For energy storage equipment, their respective operating constraints should also be met:

[0024] Electric energy storage equipment

[0025] The amount of electricity stored in the electric energy storage system should meet the following requirements:

[0026]

[0027] In the formula and are the minimum and maximum charge states of the electric energy storage device, respectively; is the capacity of the electrical energy storage device, It is the remaining power of the electric energy storage device at a certain moment;

[0028] The remaining power is calculated as follows:

[0029]

[0030] Where σ is the self-discharge rate of the energy storage device, It is the charging and discharging efficiency of the electric energy storage device;

[0031] The charging and discharging power of the electric energy storage should meet the constraints:

[0032]

[0033] In the formula and are the minimum charging and discharging power and the maximum charging and discharging power of the energy storage device, respectively. is a 0-1 variable, indicating the working status of the electric energy storage device;

[0034] Thermal energy storage equipment

[0035] Thermal energy storage capacity should meet the following requirements:

[0036]

[0037] In the formula and are the minimum and maximum heat load states of the thermal energy storage equipment, is the capacity of the thermal energy storage device, It is the residual heat of the thermal energy storage device at a certain moment;

[0038] The calculation formula for residual heat is:

[0039]

[0040] Where σ HES is the self-discharge rate of the thermal energy storage device, It is the heat storage and release efficiency of the thermal energy storage device;

[0041] The thermal energy storage and heat release power should meet the constraints:

[0042]

[0043] In the formula and are the minimum heat storage and release power and the maximum heat storage and release power of the thermal energy storage equipment, is a 0-1 variable, indicating the working status of the thermal energy storage device;

[0044] Cold energy storage equipment (ice storage air conditioner)

[0045] As a special refrigeration equipment, ice storage air conditioner consists of a refrigerator and an ice storage device, which are generally modeled separately. Among them, the refrigerator can operate in two working conditions: ice making and cooling. The refrigerator that is only used for ice making is called a single-mode refrigerator, and the refrigerator that can make ice and cool is called a dual-mode refrigerator. The dual-mode refrigerator cannot make ice and cool at the same time. Taking the dual-mode refrigerator as an example, its operation constraints are as follows:

[0046]

[0047] In the formula and are the cooling power output of the refrigerator under icing and cooling conditions, Input electrical power to the refrigerator, It is the working status symbol of the dual-mode refrigerator. and are the energy efficiency coefficients of ice-making and refrigeration conditions respectively;

[0048] The input power of the dual-mode refrigerator is limited by the rated power:

[0049]

[0050] In the formula and are the minimum input power and maximum input power of the dual-mode refrigerator;

[0051] The ice storage device is used to store the cold energy generated by the refrigeration machine. Its model is similar to that of electrical energy storage and thermal energy storage. The relationship between the storage energy before and after ice storage is:

[0052]

[0053] In the formula is the existing capacity of the ice storage device, σ CES is the self-discharge rate of the ice storage device, The charging and discharging efficiency of the ice storage device;

[0054] To ensure continuous and stable operation, the storage capacity of the ice storage device must meet the following requirements:

[0055]

[0056] In the formula and are the minimum and maximum load cooling states of the thermal energy storage equipment, is the capacity of the cold energy storage equipment, It is the remaining cooling capacity of the thermal energy storage device at a certain moment;

[0057] In addition, there are upper and lower limit constraints on the power generation of internal combustion engines and CHP units, the input power of electric refrigerators, the P2P trading plan, and the power exchanged between the microgrid and the distribution system, as follows.

[0058]

[0059]

[0060] In the formula and are the minimum and maximum output electrical power of the internal combustion engine, and are the minimum and maximum output power of the CHP unit, is the energy efficiency coefficient of the CHP unit; and are the minimum and maximum input electrical power of the electric refrigerator, They are the upper and lower limits of the P2P trading plan; is the limit of power exchange between microgrid i and the distribution system.

[0061] The formation of the power purchase transaction plan and the power sales transaction plan in step 4, the decentralized integrated energy microgrid transaction can be divided into the following five steps:

[0062] Step 1 (Transaction Release): For a certain transaction period t, each integrated energy microgrid performs its own optimization scheduling and generates its own power sales plan S according to the optimization results. i ={S i,1 …,S i,k} or Power Purchase Plan D i ={d i,1 …,d i,k};

[0063] Step 2 (Transaction Processing) For the integrated energy microgrid i with electricity sales demand, after receiving the electricity purchase plan of other integrated energy microgrids j≠i, the integrated energy microgrid with electricity purchase demand is selected according to the principle of maximizing electricity sales revenue. Its transaction processing model is shown in equations (27)-(29); where the optimization variable The power of electricity sold by microgrid i to microgrid j, kW;

[0064]

[0065] In the formula, is the electricity sales revenue of the integrated energy microgrid i, RMB; The comprehensive energy is the set of electricity purchase demands issued by the comprehensive energy microgrid j received by microgrid i. is the lth power purchase demand; S i is the collection of electricity sales resources of the integrated energy microgrid i, S i,kis the kth electricity sales resource; The power demand of the lth electricity purchase segment issued by the integrated energy microgrid j received by the integrated energy microgrid i;

[0066] For the integrated energy microgrid j with electricity purchase demand, after receiving the electricity sales notice from other integrated energy microgrids j≠i, the microgrid with electricity sales demand is selected according to the principle of minimum electricity purchase cost. Its transaction processing model is shown in equations (30)-(32):

[0067]

[0068]

[0069] In the formula, is the electricity sales revenue of integrated energy microgrid j, RMB; is the collection of electricity sales resources released by integrated energy microgrid i to integrated energy microgrid j, is the kth electricity sales resource; D j is the electricity purchasing demand set of integrated energy microgrid j, d j,K is the first electricity purchase demand; is the kth tradable power released by integrated energy microgrid i to integrated energy microgrid j;

[0070] Step 3 (Transaction Request): If the optimal solutions of the two optimization models in step 2 are both 0, it indicates that no transaction has been reached between the integrated energy microgrids i and j, and the transaction process is terminated; otherwise, a preliminary transaction request is formed between the integrated energy microgrids i and j.

[0071]

[0072] In the formula, represents the kth electricity sales resource sent by integrated energy microgrid i to integrated energy microgrid j; and ca i,k are the electricity sales power (kW) and price request (yuan / kWh) sent by integrated energy microgrid i to integrated energy microgrid j, respectively; represents the lth electricity purchase demand sent by integrated energy microgrid j to integrated energy microgrid i; and ua i,k are the power purchase power, kW and price requests sent by integrated energy microgrid j to integrated energy microgrid i, respectively;

[0073]

[0074] In the formula, The power of electric energy sold by integrated energy microgrid i to integrated energy microgrid j for electricity sales; D is the electricity sales power sent by integrated energy microgrid i to integrated energy microgrid j; j is the electricity purchasing demand set of integrated energy microgrid j, d j,l is the first electricity purchase demand; S is the purchased power sent by integrated energy microgrid j to integrated energy microgrid i; i is the collection of electricity sales resources of the integrated energy microgrid i, S i,k is the kth electricity sales resource;

[0075] Step 4 (Transaction Confirmation): Considering that in the continuous bidding market, the transaction price is updated regularly and the transaction is carried out in real time, each transaction request needs to be confirmed according to equations (37)-(38) after it is issued; the confirmed transaction will be executed during the trading period, and the transaction information of both parties will be updated; the transaction that fails to pass the confirmation will be cancelled and return to step 2;

[0076]

[0077] Step 5 (Transaction Execution): During the transaction period t, each integrated energy microgrid executes the agreed transaction; transactions that cannot be completed will be completed by the defaulting party through purchasing / selling electricity from the distribution system;

[0078] After the P2P transaction of the integrated energy microgrid group is completed, the IEMO of each integrated energy microgrid reports the transaction plan to IEMCO based on the transaction plan reached with other integrated energy microgrids, and IEMCO reports the aggregated transaction power to DSO and electricity purchased or sold to the distribution system For DSO to conduct safety verification and fee settlement, N IEM is the number of integrated energy microgrids.

[0079] The coordinated optimization dispatching model of the distribution system supporting end-to-end transactions of integrated energy microgrid groups in step 6;

[0080] The model is established for a single time t, which can be extended to multiple time periods for day-ahead optimization scheduling in actual application;

[0081] Optimization variables

[0082] 2) P2P transactions are not implemented

[0083] The optimization variable is the output of the controllable equipment under the control of the distribution system:

[0084] P1=(P EES ) T (39)

[0085] In the formula, N is the charging and discharging power of each energy storage system. A positive element in the vector indicates discharging, and a negative element indicates charging. EESis the number of electric energy storage systems;

[0086] 2) Implement P2P transactions

[0087] The optimization variables are the output of controllable equipment under the power distribution system and the reduction amount of each P2P trading plan:

[0088] P2=(P EES ,ΔP P2P ) T (40)

[0089] In the formula, The reduction amount of P2P transactions in each microgrid;

[0090] Objective Function

[0091] 1) P2P transactions are not implemented

[0092] The optimization scheduling objective function without P2P transaction is as follows:

[0093]

[0094] In the formula, is the electricity price of the distribution system; l and I l are the resistance of line l, Ω and the current, Ω L Represents the set of all lines in the system; c EES is the degradation cost coefficient of electric energy storage; Ω GEES Represents the collection of energy storage systems on the distribution network side;

[0095] 2) Implement P2P transactions

[0096] After implementing P2P transactions, the optimization scheduling target also needs to increase the penalty cost for P2P transaction reduction, as follows:

[0097]

[0098] In the formula, Ω IEM represents the set of all integrated energy microgrids in the system; ω is the penalty coefficient for P2P transaction reduction;

[0099] Constraints

[0100] 3) Distflow second-order cone power flow model:

[0101]

[0102]

[0103] Where σ(j) represents the set of branch head nodes with j as the terminal node; γ(j) represents the set of branch terminal nodes with j as the head node; Pi,j and Q i,j are the active and reactive power flowing from node i to node j respectively; P j and Q j are the active and reactive injected powers of node j, respectively; r i,j and x i,j Line L i,j The resistance and reactance, I i,j For line L i,j The current amplitude on j is the voltage amplitude at node j;

[0104] The composition of active and reactive injection power of each node is different in different dispatching models. In the optimal dispatching model of the distribution system without P2P trading, the expressions of active and reactive injection power of node i are as follows:

[0105]

[0106] In the formula, and represents the active and reactive power injected by the upper grid at node i; and represents the kW and reactive load at node i; Θ i represents the reactive and active load ratio of the integrated energy microgrid at node i;

[0107] In the distribution system optimization dispatching model implementing P2P trading, the active and reactive injection power expressions of node i are as follows:

[0108]

[0109] In the formula, represents the P2P transaction reduction of the integrated energy microgrid i;

[0110] 4) Node voltage limitation and branch current constraint:

[0111] U i,min ≤U i ≤U i,max (51)

[0112] 0≤I i,j ≤I ij,max (52)

[0113] Where U i,max , U i,min are the upper and lower limits of the voltage at node i; I ij,max is the maximum current allowed by the line;

[0114] 3) Power exchange constraints between distribution network and microgrid:

[0115] As shown in formula (26);

[0116] 4) Energy storage system capacity constraints:

[0117] As shown in formulas (8), (9), and (10);

[0118] In order to solve the model conveniently, the above power flow model is subjected to phase relaxation and second-order cone transformation; Then the branch apparent power quadratic constraint shown in equation (46) is relaxed to a conical constraint, that is:

[0119]

[0120] The node voltage and branch transmission current constraints can be expressed as:

[0121] (U i,min ) 2 ≤u i ≤(U i,max ) 2 (54)

[0122] 0≤i i,j ≤(I ij,max ) 2 (55)

[0123] Formula (45) can be expressed as:

[0124]

[0125] Formulas (8), (9), (10), (43) to (45), (49), (50), (53) to (56) constitute the Distflow second-order cone model in the two optimization scheduling models, which can be solved using mature optimization software such as CPLEX and GUROBI.

[0126] Beneficial Effects

[0127] The present invention provides a study on the optimization and dispatching of a distributed integrated energy microgrid group based on a two-way auction P2P transaction mechanism. The method is to organize the integrated energy microgrids under its jurisdiction to conduct P2P transactions through the dispatcher of the integrated energy microgrid group. The transaction plan is submitted to the dispatcher of the distribution system to calculate the transaction power transmission fee and return it. The dispatcher of the integrated energy microgrid group organizes a new round of P2P transactions based on the new transmission fee, and finally realizes the economic dispatching of the distributed integrated energy microgrid group. Compared with the existing integrated energy microgrid group optimization and dispatching method, the present invention can take into account the impact of P2P transactions between integrated energy microgrids on the network loss of the distribution system and the restrictions of the distribution system operation safety on the transaction plan, and realize the overall economic operation of the distributed integrated energy microgrid group. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0129] Figure 1 It is a flow chart of an AC / DC HDN day-ahead economic dispatch algorithm based on the Cell management mode involved in the present invention; DETAILED DESCRIPTION

[0130] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0131] The following is combined with Figure 1 The embodiments of the present invention are described as follows:

[0132] First, some concepts used in this patent are introduced:

[0133] Integrated Energy Microgrid Operator (IEMO): It is the dispatching organization of each independent integrated energy microgrid. It has access to all kinds of information within the integrated energy microgrid and can issue control instructions to each energy supply and storage device within the integrated energy microgrid to ensure the economic operation of the entire integrated energy microgrid.

[0134] Integrated Energy Microgrid Cluster Operator (IEMCO): It is the dispatching organization of the entire integrated energy microgrid cluster, responsible for coordinating transactions between the integrated energy microgrids and the distribution system to achieve the economic operation of the entire integrated energy microgrid cluster.

[0135] Distribution System Operator (DSO): The dispatching agency of the regional distribution system to which the microgrid cluster belongs, responsible for the safe operation of the distribution network where the entire microgrid cluster is located.

[0136] 1. A distributed integrated energy microgrid group optimization scheduling method based on a double auction P2P transaction mechanism includes the following steps:

[0137] Step 1: Set the scheduling time t.

[0138] Step 2: Each IEMO reads the parameters of power generation, heating / cooling, power / heat / cooling storage equipment, and electricity, heat, and cooling load data within the integrated energy microgrid under its jurisdiction.

[0139] Step 3: For the trading period t, each IEMO optimizes the scheduling of its own integrated energy microgrid and generates a power purchase and sales plan within the microgrid group based on the optimization results.

[0140] Step 4: For the integrated energy microgrid with electricity sales demand, after receiving the electricity purchase plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of maximizing electricity sales revenue, and the electricity sales transaction plan is calculated; for the integrated energy microgrid with electricity purchase demand, after receiving the electricity sales plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of minimizing electricity purchase cost, and the electricity purchase transaction plan is calculated.

[0141] Step 5: Each IEMO uploads its trading plan to IEMCO for confirmation. IEMCO will upload the confirmed P2P trading plan between microgrids and the plan of purchasing or selling electricity to the distribution system to DSO.

[0142] Step 6: Based on the distribution user load and the transaction information of the microgrid group, the DSO uses the optimization scheduling model to solve the change in system network loss cost before and after the implementation of P2P transactions and the power reduction of each P2P transaction, and sends it to the microgrid group dispatcher IEMCO.

[0143] Step 7: IEMCO determines whether the power reduction of each P2P transaction is less than the preset value. If so, it jumps out of the loop and outputs the microgrid P2P transaction plan; otherwise, it calculates the transaction power limit, updates the P2P transaction service fee, and goes to step 3.

[0144] 2. Step 3 involves an improved decentralized integrated energy microgrid group optimization scheduling model based on a double auction P2P transaction mechanism, which can be expressed as follows:

[0145] ①Objective function

[0146] In the P2P trading mode of distributed integrated energy microgrids, IEMO usually aims to maximize its own electricity supply cost and trading revenue. Taking the integrated energy microgrid containing internal combustion engines, CHP units, electric refrigeration units, electric energy storage, thermal energy storage, and cold energy storage as an example, IEMO needs to arrange the scheduling plan of internal power generation equipment, cooling and heating equipment, and energy storage equipment, as well as the P2P trading plan with other integrated energy microgrids. Considering the volatility of internal sources and loads, each integrated energy microgrid also needs to consume surplus electricity or supplement insufficient electricity through the distribution system during some periods. Taking any scheduling time t as an example, under the distributed P2P trading mode, the optimization scheduling objective function of the integrated energy microgrid i is shown in formula (1).

[0147]

[0148] In the formula, is the total power supply cost of integrated energy microgrid i at time t, RMB. is the dispatching cost within the integrated energy microgrid i, RMB. The cost of purchasing electricity from the distribution system or the revenue from selling electricity for the integrated energy microgrid i, RMB. is the expected revenue of P2P transaction of integrated energy microgrid i, RMB.

[0149] The cost calculation method of each item in formula (1) is as follows:

[0150]

[0151] In the formula is the power generation cost of the internal combustion engine (including operation and maintenance costs and gas purchase costs), RMB / kWh. is the power generation of the internal combustion engine, kW. is the power generation cost of the CHP unit (including operation and maintenance costs and gas purchase costs), RMB / kWh. is the power generation capacity of CHP unit, kW. is the operation and maintenance cost of the electric refrigerator, RMB / kWh. is the input electrical power of the electric refrigerator, kW. It is the operation and maintenance cost of energy storage equipment (electricity, heat and cold energy storage), RMB / kWh. are the energy release and storage power of the energy storage device, kW. Ω MT ,Ω CHP ,Ω EC ,Ω eS They are the collection of internal combustion engines, combined heat and power units, electric refrigerators, and energy storage devices. DS The electricity sales price or electricity purchase price of the distribution system is selected according to the transaction status between the integrated energy microgrid and the distribution system, RMB. is the transaction power between the integrated energy microgrid and the distribution system, kW. and They are the market transaction electricity price between integrated energy microgrids and the service fee charged by the distribution system, RMB. is the transaction power between integrated energy microgrids, kW. It is calculated by the basic service fee and the network loss cost caused by P2P transactions. It takes a positive value for the electricity seller and a negative value for the electricity buyer. Δt is the scheduling cycle time interval, h.

[0152] ②Constraints

[0153] The optimal dispatch of the integrated energy microgrid i needs to meet the following power balance constraints, including the electric bus balance constraint, the thermal bus balance constraint, and the cold bus balance constraint:

[0154]

[0155] In the formula and are the discharge power and charging power of the electric energy storage device, kW respectively. is the power consumption in the integrated energy microgrid i, kW. Ω EES A collection of electrical energy storage devices. and are the heat release power and heat storage power of the thermal energy storage equipment, kW respectively. is the heat power in the integrated energy microgrid i, kW. Ω HES A collection of thermal energy storage devices. and They are the cooling power and cold storage power of the cold storage equipment, kW respectively. is the cooling power in the integrated energy microgrid i, kW. Ω EC A collection of electric refrigerators. is the cooling coefficient of the electric refrigerator. The input power of the ice storage device (ice storage air conditioner). Ω CES A collection of ice storage air conditioners.

[0156] For energy storage equipment, their respective operating constraints should also be met.

[0157] Electric energy storage equipment

[0158] The amount of electricity stored in the electric energy storage system should meet the following requirements:

[0159]

[0160] In the formula and They are the minimum and maximum states of charge of the energy storage device respectively. is the capacity of the electrical energy storage device, kWh. It is the remaining power of the energy storage device at a certain moment, kWh.

[0161] The remaining power is calculated as follows:

[0162]

[0163] Where σ is the self-discharge rate of the energy storage device. It is the charging and discharging efficiency of the electrical energy storage device.

[0164] The charging and discharging power of the electric energy storage should meet the constraints:

[0165]

[0166] In the formula and They are the minimum charging and discharging power and the maximum charging and discharging power of the electric energy storage device, kW. It is a 0-1 variable, indicating the working status of the electric energy storage device.

[0167] Thermal energy storage equipment

[0168] Thermal energy storage capacity should meet the following requirements:

[0169]

[0170] In the formula and They are the minimum and maximum heat load states of the thermal energy storage equipment respectively. is the capacity of the thermal energy storage device, kWh. It is the residual heat of the thermal energy storage device at a certain moment, kWh.

[0171] The calculation formula for residual heat is:

[0172]

[0173] Where σ HES is the self-discharge rate of the thermal energy storage device. It is the heat storage and release efficiency of the thermal energy storage equipment.

[0174] The thermal energy storage and heat release power should meet the constraints:

[0175]

[0176] In the formula and They are the minimum storage and release power and the maximum storage and release power of the thermal energy storage equipment, kW. It is a 0-1 variable, indicating the working status of the thermal energy storage device.

[0177] Cold energy storage equipment (ice storage air conditioner)

[0178] As a special refrigeration equipment, ice storage air conditioner consists of a refrigerator and an ice storage device, which are generally modeled separately. Among them, the refrigerator can operate in two working conditions: ice making and cooling. The refrigerator that is only used for ice making is called a single-mode refrigerator, and the refrigerator that can make ice and cool is called a dual-mode refrigerator. The dual-mode refrigerator cannot make ice and cool at the same time. Taking the dual-mode refrigerator as an example, its operation constraints are as follows.

[0179]

[0180] In the formula and are the cooling power output of the refrigerator under icing and cooling conditions, Input electrical power to the refrigerator, It is the working status symbol of the dual-mode refrigerator. and are the energy efficiency coefficients of ice making and refrigeration respectively.

[0181] The input power of the dual-mode refrigerator is limited by the rated power:

[0182]

[0183] In the formula and It is the minimum input power and maximum input power of the dual-mode refrigerator.

[0184] The ice storage device is used to store the cold energy generated by the refrigeration machine. Its model is similar to that of electrical energy storage and thermal energy storage. The relationship between the storage energy before and after ice storage is:

[0185]

[0186] In the formula is the current capacity of the ice storage device, kWh. CES is the self-discharge rate of the ice storage device. It is the charging and discharging energy efficiency of the ice storage device.

[0187] To ensure continuous and stable operation, the storage capacity of the ice storage device must meet the following requirements:

[0188]

[0189] In the formula and They are the minimum and maximum load cooling states of the thermal energy storage equipment respectively. is the capacity of the cold energy storage device, kWh. It is the remaining cooling capacity of the thermal energy storage device at a certain moment, kWh.

[0190] In addition, there are upper and lower limit constraints on the power generation of internal combustion engines and CHP units, the input power of electric refrigerators, the P2P trading plan, and the power exchanged between the microgrid and the distribution system, as follows.

[0191]

[0192] In the formula and They are the minimum and maximum output electrical power of the internal combustion engine, kW. and They are the minimum and maximum output power of the CHP unit, kW. is the energy efficiency coefficient of the CHP unit. and are the minimum and maximum input electrical power of the electric refrigerator, kW. They are the upper and lower limits of the P2P transaction plan, kW; is the limit of power exchange between microgrid i and distribution system, kW.

[0193] 3. An improved decentralized integrated energy microgrid transaction task formation model based on a double auction P2P transaction mechanism involved in step 4 can be expressed as follows:

[0194] Step 1 (Transaction Release): For a certain transaction period t, each integrated energy microgrid performs its own optimization scheduling and generates its own power sales plan S according to the optimization results. i ={S i,1 …,S i,k} or Power Purchase Plan D i ={d i,1 …,d i,k};

[0195] Step 2 (Transaction Processing) For the integrated energy microgrid i with electricity sales demand, after receiving the electricity purchase plan of other integrated energy microgrids j≠i, the integrated energy microgrid with electricity purchase demand is selected according to the principle of maximizing electricity sales revenue. Its transaction processing model is shown in equations (27)-(29). Among them, the optimization variable The power sold by microgrid i to microgrid j, kW.

[0196]

[0197] In the formula, is the electricity sales revenue of the integrated energy microgrid i, RMB; The comprehensive energy is the set of electricity purchase demands issued by the comprehensive energy microgrid j received by microgrid i. is the lth power purchase demand; S iis the collection of electricity sales resources of the integrated energy microgrid i, S i,k is the kth electricity sales resource; The power demand of the lth power purchase segment issued by integrated energy microgrid j received by integrated energy microgrid i, kW.

[0198] For the integrated energy microgrid j with electricity purchase demand, after receiving the electricity sales notice from other integrated energy microgrids j≠i, the microgrid with electricity sales demand is selected according to the principle of minimizing the electricity purchase cost. Its transaction processing model is shown in Equations (30)-(32).

[0199]

[0200] In the formula, is the electricity sales revenue of integrated energy microgrid j, RMB; is the collection of electricity sales resources released by integrated energy microgrid i to integrated energy microgrid j, is the kth electricity sales resource; D j is the electricity purchasing demand set of integrated energy microgrid j, d j,l is the first electricity purchase demand; is the kth tradable power released by integrated energy microgrid i to integrated energy microgrid j, kW.

[0201] Step 3 (Transaction Request): If the optimal solutions of the two optimization models in step 2 are both 0, it indicates that no transaction has been reached between the integrated energy microgrids i and j, and the transaction process is terminated. Otherwise, a preliminary transaction request is formed between the integrated energy microgrids i and j.

[0202]

[0203] In the formula, represents the kth electricity sales resource sent by integrated energy microgrid i to integrated energy microgrid j; and ca i,k are the electricity sales power (kW) and price request (yuan / kWh) sent by integrated energy microgrid i to integrated energy microgrid j, respectively; represents the lth electricity purchase demand sent by integrated energy microgrid j to integrated energy microgrid i; and ua i,k They are the power purchase power (kW) and price request (yuan / kWh) sent by integrated energy microgrid j to integrated energy microgrid i.

[0204]

[0205] In the formula, The power of electric energy sold by integrated energy microgrid i to integrated energy microgrid j, kW; is the electricity sales power sent by integrated energy microgrid i to integrated energy microgrid j, kW; ≠ is the electricity purchase demand set of integrated energy microgrid j, d j,l is the first electricity purchase demand; is the purchased power sent by integrated energy microgrid j to integrated energy microgrid i, kW; S i is the collection of electricity sales resources of the integrated energy microgrid i, S i,k is the kth electricity sales resource.

[0206] Step 4 (Transaction Confirmation): Considering that in the continuous bidding market, the transaction price is updated regularly and the transaction is carried out in real time, each transaction request needs to be confirmed according to equations (37)-(38) after it is issued. The confirmed transaction will be executed during the trading period and the transaction information of both parties will be updated; the transaction that fails to pass the confirmation will be cancelled and return to step 2.

[0207]

[0208] Step 5 (Transaction Execution): During the transaction period t, each integrated energy microgrid executes the agreed transaction. The transaction that cannot be completed will be completed by the defaulting party through purchasing / selling electricity from the distribution system.

[0209] After the P2P transaction of the integrated energy microgrid group is completed, the IEMO of each integrated energy microgrid reports the transaction plan to IEMCO based on the transaction plan reached with other integrated energy microgrids, and IEMCO reports the aggregated transaction power to DSO and electricity purchased or sold to the distribution system For DSO to conduct safety verification and fee settlement, N IEM is the number of integrated energy microgrids.

[0210] 4. The distribution system collaborative optimization scheduling model supporting end-to-end transactions of integrated energy microgrid groups in step 6 can be expressed as follows:

[0211] The goal of optimizing the dispatching of the distribution system is to minimize the operating cost. This patent mainly considers network loss and energy storage degradation cost. In order to calculate the impact of P2P transactions on system network loss and energy storage degradation cost, this patent proposes the following two models for optimizing the dispatching of the distribution system before and after the implementation of P2P transactions. To simplify the description, the following model is only established for a single time t, which can be extended to multiple time periods for day-ahead optimization dispatching in actual application.

[0212] ①Optimize variables

[0213] 3) P2P transactions are not implemented

[0214] The optimization variable is the output of the controllable equipment (this section takes the electric energy storage system as an example) governed by the distribution system:

[0215] P1=(P EES ) T (39)

[0216] In the formula, N is the charging and discharging power of each energy storage system, kW. A positive element in the vector indicates discharging, and a negative element indicates charging. EES is the number of electric energy storage systems.

[0217] 2) Implement P2P transactions

[0218] The optimization variables are the output of controllable equipment under the power distribution system and the reduction amount of each P2P trading plan:

[0219] P2=(P EES ,ΔP P2P ) T (40)

[0220] In the formula, is the reduction amount of P2P transactions in each microgrid, kW.

[0221] ②Objective function

[0222] 1) P2P transactions are not implemented

[0223] The optimization scheduling objective function without P2P transaction is as follows:

[0224]

[0225] In the formula, is the electricity price of the distribution system, RMB / kWh; r l and I l are the resistance of line l, Ω and current, kA; Ω L Represents the set of all lines in the system; c EES is the degradation cost coefficient of electric energy storage, RMB / kWh; Ω GEES Represents the collection of energy storage systems on the distribution grid side.

[0226] 2) Implement P2P transactions

[0227] After implementing P2P transactions, the optimization scheduling target also needs to increase the penalty cost for P2P transaction reduction, as follows:

[0228]

[0229] In the formula, Ω IEM represents the set of all integrated energy microgrids in the system; ω is the penalty coefficient for P2P transaction reduction.

[0230] ③Constraints

[0231] 5) Distflow second-order cone power flow model:

[0232]

[0233] Where σ(j) represents the set of branch head nodes with j as the terminal node; γ(j) represents the set of branch terminal nodes with j as the head node; P i,j and Q i,j are the active power, kW, and reactive power, kVar, flowing from node i to node j; P j and Q j are the active power, kW, and reactive power, kVar, of node j, respectively; r i,j and x i,j Line L i,j Resistance, Ω and reactance, Ω; I i,j For line L i,j Current amplitude on, kA; U j is the voltage amplitude at node j, kV.

[0234] The composition of active and reactive injection power of each node is different in different dispatching models. In the distribution system optimization dispatching model without P2P trading, the expressions of active and reactive injection power of node i are as follows:

[0235]

[0236] In the formula, and represents the active power, kW, and reactive power, kVar, injected into the upper grid at node i; and represents the active, kW and reactive loads at node i, kVar; Θ i Represents the reactive and active load ratio of the integrated energy microgrid at node i.

[0237] In the distribution system optimization dispatching model implementing P2P trading, the active and reactive injection power expressions of node i are as follows:

[0238]

[0239] In the formula, It represents the P2P transaction reduction of integrated energy microgrid i, kW.

[0240] 6) Node voltage limitation and branch current constraint:

[0241] U i,min ≤U i ≤U i,max (51)

[0242] 0≤I i,j ≤I ij,max (52)

[0243] Where U i,max , U i,min are the upper and lower limits of the voltage at node i, kV; I ij,max is the maximum current allowed by the line, kA.

[0244] 3) Power exchange constraints between distribution network and microgrid:

[0245] As shown in formula (26).

[0246] 4) Energy storage system capacity constraints:

[0247] As shown in formulas (8), (9) and (10).

[0248] In order to solve the model conveniently, the above power flow model is subjected to phase relaxation and second-order cone transformation. Then the branch apparent power quadratic constraint shown in equation (46) is relaxed to a conical constraint, that is:

[0249]

[0250] The node voltage and branch transmission current constraints can be expressed as:

[0251] (U i,min ) 2 ≤u i ≤(U i,max ) 2 (54)

[0252] 0≤i i,j ≤(I ij,max ) 2 (55)

[0253] Formula (45) can be expressed as:

[0254]

[0255] 4. The integrated energy microgrid exchange power reduction model in step 7 can be expressed as follows:

[0256] According to formula (57), it is judged whether the exchange power reduction of each integrated energy microgrid is less than the preset value ε. If so, the loop is jumped out and the integrated energy microgrid P2P transaction plan and distribution system scheduling plan are output; otherwise, each integrated energy microgrid calculates the transaction power limit according to formula (58), updates the P2P transaction service fee according to formula (59), and proceeds to step 3.

[0257] The iteration termination condition is shown in formula (57):

[0258]

[0259] Where ε is the preset exchange power reduction threshold, kW.

[0260] The correction formula for the transaction power limit of each integrated energy microgrid is as follows:

[0261]

[0262] In the formula, and are the upper and lower limits of the P2P transaction power of the k-th iteration integrated energy microgrid i, kW; is the P2P transaction power correction of the k-th iteration integrated energy microgrid i, kW.

[0263] P2P transaction service fee for the kth iteration in period t The calculation formula is as follows:

[0264]

[0265] Where, ΔC DS The change in system network loss and energy storage degradation cost before and after the implementation of P2P trading, RMB; is the total P2P transaction power, kW.

[0266] At the same time, considering the P2P arbitrage demands of integrated energy microgrids, the P2P transaction service fee Make the following restrictions:

[0267]

[0268] In the formula, is the electricity price of the distribution system, RMB / kWh; It is the electricity purchase price for the distribution system, RMB / kWh.

[0269] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A decentralized integrated energy microgrid group optimization scheduling method based on a double auction P2P transaction mechanism, characterized in that: The following steps are involved: Step 1: Set the scheduling time t; Step 2: Each IEMO reads the parameters of power generation, heating / cooling, power / heat / cooling storage equipment, and electricity, heating, and cooling load data within the integrated energy microgrid under its jurisdiction; Step 3: For the trading period t, each IEMO optimizes the scheduling of its own integrated energy microgrid and generates a power purchase and sales plan within the microgrid group based on the optimization results; Step 4: For the integrated energy microgrid with electricity sales demand, after receiving the electricity purchase plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of maximizing electricity sales revenue, and the electricity sales transaction plan is calculated; For the integrated energy microgrid with electricity purchase demand, after receiving the electricity sales plans of other microgrids, the microgrid with electricity sales demand is selected according to the principle of minimizing electricity purchase cost, and the electricity purchase transaction plan is calculated; Step 5: Each IEMO uploads its own trading plan to IEMCO for confirmation; IEMCO uploads the confirmed P2P trading plan between microgrids and the plan of purchasing or selling electricity to the distribution system to DSO; Step 6: Based on the distribution user load and the transaction information of the microgrid group, the DSO uses the optimization dispatch model to solve the change in system network loss cost before and after the implementation of P2P transactions and the power reduction of each P2P transaction, and sends it to the microgrid group dispatcher IEMCO; Step 7: IEMCO determines whether the power reduction of each P2P transaction is less than the preset value ε. If so, it jumps out of the loop and outputs the microgrid P2P transaction plan; otherwise, it calculates the transaction power limit, updates the P2P transaction service fee of each integrated energy microgrid, and goes to step 3.

2. The method for optimizing and scheduling a distributed integrated energy microgrid based on a double auction P2P transaction mechanism according to claim 1 is characterized in that: The step 3 adopts a comprehensive energy microgrid optimization scheduling model of P2P transactions; Taking any dispatching time t as an example, under the P2P trading mode, the optimal dispatching objective function of the integrated energy microgrid i is shown in formula (1): In the formula, is the total power supply cost of integrated energy microgrid i at time t, is the dispatching cost within the integrated energy microgrid i, The cost of purchasing electricity from the distribution system or the revenue of selling electricity for the integrated energy microgrid i, is the expected revenue of P2P transactions of integrated energy microgrid i; The cost calculation method of each item in formula (1) is as follows: In the formula is the power generation cost of the internal combustion engine, is the power generation of the internal combustion engine, is the power generation cost of the CHP unit, is the power generation capacity of the CHP unit, is the operation and maintenance cost of the electric refrigerator, is the input power of the electric refrigerator, is the operation and maintenance cost of the energy storage equipment, are the energy release and storage power of the energy storage device, Ω MT ,Ω CHP ,Ω EC ,Ω ES They are respectively a collection of internal combustion engines, combined heat and power units, electric refrigerators, and energy storage devices; c DS According to the transaction status between the integrated energy microgrid and the distribution system, the electricity sales price or electricity purchase price of the distribution system is selected. Trading electric power for integrated energy microgrids and distribution systems, and They are the market transaction electricity price between integrated energy microgrids and the service fee charged by the distribution system. It is the trading power between integrated energy microgrids. It is calculated from the basic service fee and the network loss fee caused by P2P transactions. It takes a positive value for the electricity seller and a negative value for the electricity buyer. Δt is the time interval of the dispatch cycle. The optimal dispatch of the integrated energy microgrid i needs to meet the following power balance constraints, including the electric bus balance constraint, the thermal bus balance constraint, and the cold bus balance constraint: In the formula and are the discharge power and charging power of the energy storage device, respectively. is the power consumption in the integrated energy microgrid i, Ω EES A collection of electrical energy storage devices; and are the heat release power and heat storage power of the thermal energy storage equipment, is the thermal power used in the integrated energy microgrid i, Ω HES A collection of thermal energy storage devices; and are the cooling power and cold storage power of the cold storage equipment, is the cooling power in the integrated energy microgrid i, Ω EC A collection of electric refrigerators; is the refrigeration coefficient of the electric refrigerator, is the input power of the ice storage device (ice storage air conditioner), Ω CES A collection of ice storage air conditioners; For energy storage equipment, their respective operating constraints should also be met: Electric energy storage equipment The amount of electricity stored in the electric energy storage system should meet the following requirements: In the formula and are the minimum and maximum charge states of the electric energy storage device, respectively; is the capacity of the electrical energy storage device, It is the remaining power of the energy storage device at a certain moment; The remaining power is calculated as follows: Where σ is the self-discharge rate of the energy storage device, It is the charging and discharging efficiency of the electric energy storage device; The charging and discharging power of the energy storage should meet the constraints: In the formula and are the minimum charging and discharging power and the maximum charging and discharging power of the energy storage device, respectively. is a 0-1 variable, indicating the working status of the electric energy storage device; Thermal energy storage equipment Thermal energy storage capacity should meet the following requirements: In the formula and are the minimum and maximum heat load states of the thermal energy storage equipment, is the capacity of the thermal energy storage device, It is the residual heat of the thermal energy storage device at a certain moment; The calculation formula for residual heat is: Where σ HES is the self-discharge rate of the thermal energy storage device, It is the heat storage and release efficiency of the thermal energy storage device; The thermal energy storage and heat release power should meet the constraints: In the formula and are the minimum heat storage and release power and the maximum heat storage and release power of the thermal energy storage equipment, is a 0-1 variable, indicating the working status of the thermal energy storage device; Cold energy storage equipment (ice storage air conditioner) As a special refrigeration equipment, ice storage air conditioner consists of a refrigerator and an ice storage device, which are generally modeled separately. Among them, the refrigerator can operate in two working conditions: ice making and cooling. The refrigerator that is only used for ice making is called a single-mode refrigerator, and the refrigerator that can make ice and cool is called a dual-mode refrigerator. The dual-mode refrigerator cannot make ice and cool at the same time. Taking the dual-mode refrigerator as an example, its operation constraints are as follows: In the formula and are the cooling power output of the refrigerator under icing and cooling conditions, Input electrical power to the refrigerator, It is the working status symbol of the dual-mode refrigerator. and are the energy efficiency coefficients of ice making and refrigeration conditions respectively; The input power of the dual-mode refrigerator is limited by the rated power: In the formula and are the minimum input power and maximum input power of the dual-mode refrigerator; The ice storage device is used to store the cold energy generated by the refrigeration machine. Its model is similar to that of electrical energy storage and thermal energy storage. The relationship between the storage energy before and after ice storage is: In the formula is the existing capacity of the ice storage device, σ CES is the self-discharge rate of the ice storage device, The charging and discharging efficiency of the ice storage device; To ensure continuous and stable operation, the storage capacity of the ice storage device must meet the following requirements: In the formula and are the minimum and maximum load cooling states of the thermal energy storage equipment, is the capacity of the cold energy storage equipment, It is the remaining cooling capacity of the thermal energy storage device at a certain moment; In addition, there are upper and lower limit constraints on the power generation of internal combustion engines and CHP units, the input power of electric refrigerators, the P2P trading plan, and the power exchanged between the microgrid and the distribution system, as follows. In the formula and are the minimum and maximum output electrical power of the internal combustion engine, and are the minimum and maximum output power of the CHP unit, is the energy efficiency coefficient of the CHP unit; and are the minimum and maximum input electrical power of the electric refrigerator, They are the upper and lower limits of the P2P trading plan; is the limit of power exchange between microgrid i and the distribution system.

3. The method for optimizing and scheduling a distributed integrated energy microgrid based on a double auction P2P transaction mechanism according to claim 1 is characterized in that: The formation of the power purchase transaction plan and the power sales transaction plan in step 4, the decentralized integrated energy microgrid transaction can be divided into the following five steps: Step 1 (Transaction Release): For a certain transaction period t, each integrated energy microgrid performs its own optimization scheduling and generates its own power sales plan S according to the optimization results. i ={S i,1 …,S i,k } or Power Purchase Plan D i ={d i,1 …,d i,k }; Step 2 (Transaction Processing) For the integrated energy microgrid i with electricity sales demand, after receiving the electricity purchase plan of other integrated energy microgrids j≠i, the integrated energy microgrid with electricity purchase demand is selected according to the principle of maximizing electricity sales revenue. Its transaction processing model is shown in equations (27)-(29); where the optimization variable The power of electricity sold by microgrid i to microgrid j, kW; In the formula, is the electricity sales revenue of the integrated energy microgrid i, RMB; The comprehensive energy is the set of electricity purchase demands issued by the comprehensive energy microgrid j received by microgrid i. is the lth power purchase demand; S i is the collection of electricity sales resources of the integrated energy microgrid i, S i,k is the kth electricity sales resource; The power demand of the lth electricity purchase segment issued by the integrated energy microgrid j received by the integrated energy microgrid i; For the integrated energy microgrid j with electricity purchase demand, after receiving the electricity sales notice from other integrated energy microgrids j≠i, the microgrid with electricity sales demand is selected according to the principle of minimum electricity purchase cost. Its transaction processing model is shown in equations (30)-(32): In the formula, is the electricity sales revenue of integrated energy microgrid j, RMB; is the collection of electricity sales resources released by integrated energy microgrid i to integrated energy microgrid j, is the kth electricity sales resource; D j is the electricity purchasing demand set of integrated energy microgrid j, d j,l is the first electricity purchase demand; is the kth tradable power released by integrated energy microgrid i to integrated energy microgrid j; Step 3 (Transaction Request): If the optimal solutions of the two optimization models in step 2 are both 0, it indicates that no transaction has been reached between the integrated energy microgrids i and j, and the transaction process is terminated; otherwise, a preliminary transaction request is formed between the integrated energy microgrids i and j. In the formula, represents the kth electricity sales resource sent by integrated energy microgrid i to integrated energy microgrid j; and ca i,k are the electricity sales power (kW) and price request (yuan / kWh) sent by integrated energy microgrid i to integrated energy microgrid j, respectively; represents the lth electricity purchase demand sent by integrated energy microgrid j to integrated energy microgrid i; and ua i,k are the power purchase power, kW and price requests sent by integrated energy microgrid j to integrated energy microgrid i, respectively; In the formula, The power of electric energy sold by integrated energy microgrid i to integrated energy microgrid j for electricity sales; D is the electricity sales power sent by integrated energy microgrid i to integrated energy microgrid j; j is the electricity purchasing demand set of integrated energy microgrid j, d j,l is the first electricity purchase demand; S is the purchased power sent by integrated energy microgrid j to integrated energy microgrid i; i is the collection of electricity sales resources of the integrated energy microgrid i, S i,k is the kth electricity sales resource; Step 4 (Transaction Confirmation): Considering that in the continuous bidding market, the transaction price is updated regularly and the transaction is carried out in real time, each transaction request needs to be confirmed according to equations (37)-(38) after it is issued; the confirmed transaction will be executed during the trading period, and the transaction information of both parties will be updated; the transaction that fails to pass the confirmation will be cancelled and return to step 2; Step 5 (Transaction Execution): During the transaction period t, each integrated energy microgrid executes the agreed transaction; transactions that cannot be completed will be completed by the defaulting party through purchasing / selling electricity from the distribution system; After the P2P transaction of the integrated energy microgrid group is completed, the IEMO of each integrated energy microgrid reports the transaction plan to IEMCO based on the transaction plan reached with other integrated energy microgrids, and IEMCO reports the aggregated transaction power to DSO and electricity purchased or sold to the distribution system For DSO to conduct safety verification and fee settlement, N IEM is the number of integrated energy microgrids.

4. The method for optimizing and scheduling a distributed integrated energy microgrid based on a double auction P2P transaction mechanism according to claim 1 is characterized in that: The distribution system collaborative optimization scheduling model supporting end-to-end transactions of integrated energy microgrid groups in step 6; The model is established for a single time t, which can be extended to multiple time periods for day-ahead optimization scheduling in actual application; Optimization variables 1) P2P transactions are not implemented The optimization variable is the output of the controllable equipment under the control of the distribution system: P1=(P EES ) T (39) In the formula, N is the charging and discharging power of each energy storage system. A positive element in the vector indicates discharging, and a negative element indicates charging. EES is the number of electric energy storage systems; 2) Implement P2P transactions The optimization variables are the output of controllable equipment under the power distribution system and the reduction amount of each P2P trading plan: P2=(P EES ,ΔP P2P ) T (40) In the formula, The reduction amount of P2P transactions in each microgrid; Objective Function 1) P2P transactions are not implemented The optimization scheduling objective function without P2P transaction is as follows: In the formula, is the electricity price of the distribution system; l and I l are the resistance of line l, Ω and the current, Ω L Represents the set of all lines in the system; c EES is the degradation cost coefficient of electric energy storage; Ω GEES Represents the collection of energy storage systems on the distribution network side; 2) Implement P2P transactions After implementing P2P transactions, the optimization scheduling target also needs to increase the penalty cost for P2P transaction reduction, as follows: In the formula, Ω IEM represents the set of all integrated energy microgrids in the system; ω is the penalty coefficient for P2P transaction reduction; Constraints 1) Distflow second-order cone power flow model: Where σ(j) represents the set of branch head nodes with j as the terminal node; γ(j) represents the set of branch terminal nodes with j as the head node; P i,j and Q i,j are the active and reactive power flowing from node i to node j respectively; P j and Q j are the active and reactive injected powers of node j respectively; r i,j and x i,j Line L i,j The resistance and reactance, I i,j For line L i,j The current amplitude on j is the voltage amplitude at node j; The composition of active and reactive injection power of each node is different in different dispatching models. In the optimal dispatching model of the distribution system without P2P trading, the expressions of active and reactive injection power of node i are as follows: In the formula, and represents the active and reactive power injected by the upper power grid at node i; and represents the kW and reactive load at node i; Θ i represents the reactive and active load ratio of the integrated energy microgrid at node i; In the distribution system optimization dispatching model implementing P2P trading, the active and reactive injection power expressions of node i are as follows: In the formula, represents the P2P transaction reduction of the integrated energy microgrid i; 2) Node voltage limitation and branch current constraint: IN i,min ≤U i ≤U i,max (51) 0≤I i,j ≤I ij,max (52) Where U i,max , U i,min are the upper and lower limits of the voltage at node i; I ij,max is the maximum current allowed by the line; 3) Power exchange constraints between distribution network and microgrid: As shown in formula (26); 4) Energy storage system capacity constraints: As shown in formulas (8), (9), and (10); In order to solve the model conveniently, the above power flow model is subjected to phase relaxation and second-order cone transformation; Then the branch apparent power quadratic constraint shown in equation (46) is relaxed to a conical constraint, that is: The node voltage and branch transmission current constraints can be expressed as: (IN i,min ) 2 in i ≤(U i,max ) 2 (54) 0≤i i,j ≤(I ij,max ) 2 (55) Formula (45) can be expressed as: Formulas (8), (9), (10), (43) to (45), (49), (50), (53) to (56) constitute the Distflow second-order cone model in the two optimization scheduling models, which can be solved using mature optimization software such as CPLEX and GUROBI.

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