A multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology

Abstract

A multi-energy microgrid group trading mechanism and a day-ahead optimization scheduling method based on blockchain technology include the following steps: Step 1: Building a multi-energy microgrid group trading decentralized platform based on blockchain technology, which can effectively provide an effective solution to the privacy issue of information in the transaction process through P2P networks, smart contracts, and distributed storage technology; Step 2: Building a microgrid group trading mechanism based on smart contracts, which is suitable for each microgrid operator to coordinate the interests of multiple parties in market transactions through a multi-round two-way trading mechanism; Step 3: In the day-ahead scheduling stage, each microgrid node formulates a day-ahead scheduling plan based on its own production capacity and load demand, uploads surplus and shortage power data to the smart contract, and obtains each microgrid quotation according to a preset quotation formula; The present invention provides a multi-energy microgrid group trading mechanism and a day-ahead optimization scheduling method based on blockchain technology, which can safely and efficiently schedule microgrids.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid group optimization and scheduling, and in particular to a multi-energy microgrid group transaction mechanism and a day-ahead optimization and scheduling method based on blockchain technology. Background Art

[0002] As my country vigorously promotes the construction of a clean, low-carbon, safe, and efficient modern energy system, multi-energy microgrid systems, which can organically coordinate and optimize the production, dispatch, trading, and consumption of various energy sources, have emerged. However, the complexity of multi-energy microgrid energy sources and the uncertainty of distributed energy output make optimal dispatching of multi-energy microgrids difficult, and also limit the ability of independently operated microgrids to absorb renewable energy. Therefore, how to coordinate system dispatch and renewable energy absorption to promote the economic benefits of individual microgrids through safe and efficient electricity trading mechanisms has become a key issue in the development of multi-energy microgrid clusters. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a multi-energy microgrid group trading mechanism and a day-ahead optimization scheduling method based on blockchain technology to schedule microgrids safely and efficiently.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is: a multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology, characterized by comprising the following steps:

[0005] Step 1: Build a decentralized multi-energy microgrid transaction platform based on blockchain technology. Through P2P networks, smart contracts, and distributed storage technologies, it can effectively provide an effective solution to the privacy issues of information during transactions.

[0006] Step 2: Build a microgrid group transaction mechanism based on smart contracts, which is applicable to the multi-round two-way transaction mechanism of each microgrid operator to coordinate the interests of multiple parties in market transactions;

[0007] Step 3: In the day-ahead dispatch phase, each microgrid node formulates a day-ahead dispatch plan based on its own production capacity and load demand, uploads surplus and shortage power data to the smart contract, and obtains the quote of each microgrid according to the preset quote formula;

[0008] Step 4: After each microgrid has submitted its bid, the bids of the buyer and seller microgrids are ranked accordingly according to the sequence preset in the smart contract and the best buyer and seller are selected;

[0009] Step 5: To balance the interests of different nodes and prevent malicious competition such as price monopoly, the benefits between microgrids are balanced by constructing equity coefficients.

[0010] Step 6: Based on the characteristics of market transactions and dispatch, a multi-energy microgrid day-ahead optimal dispatch model with transactions is established;

[0011] Step 7: After completing the matching transaction, both parties use the multi-energy microgrid day-ahead optimization scheduling model to update the day-ahead scheduling plan according to the transaction plan, and perform simulation verification based on the multi-energy microgrid day-ahead scheduling model.

[0012] The framework of the decentralized multi-energy microgrid group transaction platform is:

[0013] (1) The decentralized operation mode of the P2P network can form a multi-party co-governance network by treating each microgrid operator as an independent node. Each microgrid node acts as both a server and a client, and can enter and exit the blockchain network at any time during the transaction time. The multi-energy microgrid group transaction process uses a decentralized client, which can effectively avoid network attacks and ensure the decentralization and security of the transaction system;

[0014] (2) Through distributed storage technology, each microgrid operator at an independent node stores complete blockchain data, including each microgrid operator's ID, energy type, transaction amount, transaction power, etc. When data is lost, it can be restored through data from other nodes, effectively preventing the problem of data loss and irrecoverable data in multi-microgrid transactions;

[0015] (3) Smart contracts are programs on the blockchain with preset rules and trigger conditions that allow nodes to complete transactions without a third party. Each microgrid node executes the transaction mechanism function embedded in the smart contract by submitting a transaction request, triggering state changes through preset responses, and writing the process to the blockchain for storage.

[0016] The microgrid group transaction mechanism based on smart contracts is:

[0017] In a multi-round two-way trading mechanism, each market participant can be both a buyer and a seller based on their own production capacity, freely conducting many-to-many transactions, with buyers and sellers optimally matched based on their bids. In this trading mechanism, once a price is matched (instant transactions are completed through smart contracts), there is no need to consider the impact of bid time, and buyers and sellers are ranked and matched based on the principle of "highest bidder wins";

[0018] The transaction matching process of the multi-round transaction mechanism is as follows:

[0019] (1) The smart contract divides each microgrid into buyers and sellers based on the amount of electricity to be traded input by each microgrid and formulates differentiated quotations based on the preset quotation formula;

[0020] (2) In the transaction matching process, the buyer with the highest bid and the seller with the lowest bid in each round are regarded as the best transaction parties, the transaction volume is the party with less volume to be traded, and the average of the two bids is taken as the transaction price of this round of transactions;

[0021] (3) After each round of transactions is completed, the smart contract recalculates the quotation and the equity coefficient value based on the remaining electricity to be traded and enters the next round of transactions. When there is no remaining electricity to be sold or purchased at time t, the transaction will proceed to time t+1;

[0022] Preferably, the preset quotation formula in step 3 is:

[0023]

[0024] Where: μ is the microgrid preference parameter, which is set to 0.5; λ Buy,min ,λ Sell,min is the minimum price of electricity purchased or sold by each microgrid to the grid; Buy,t ,λ Sell,t is the electricity price purchased from the distribution network at time t; Purchase and sell electricity from the higher-level power grid for both buyer and seller microgrids; It is the microgrid electricity load demand of both buyers and sellers.

[0025] Preferably, the method for selecting the best buyer and seller is:

[0026] After each microgrid quotes, the prices of the buyer microgrid queue j∈B={1,2,L} ​​are arranged from high to low according to formula (2), and the prices of the seller microgrid queue i∈S={1,2,L} ​​are arranged from low to high, and the buyer with the highest bid is selected. The buyer is J, the best buyer, and the seller has the lowest bid. The seller is the best seller I;

[0027]

[0028] Where: is the nth round of quotation at time t, when n=1,

[0029] After selecting the best two parties in this round, if the seller has the lowest bid Greater than or equal to the highest bid among buyers If the precondition (3) holds, the transaction between seller i and buyer j is successful, and the transaction price of each round of transaction is and transaction volume Calculated by formula (4) and formula (5) respectively:

[0030]

[0031]

[0032]

[0033] Where, are the electricity to be traded of the optimal buyer and seller respectively;

[0034] After each round of trading, the remaining electricity to be traded between the buyer and the seller is determined by the preset formula (6):

[0035]

[0036] In the formula, OR(A,B) means that if either A or B is 0 or both are 0, the output is 1, and a new round of trading begins at time t+1; otherwise, the output is 0, and the trading continues in round n+1 at time t.

[0037] Preferably, the equity coefficient is constructed as follows:

[0038] The equity coefficient is composed of the equity value of participating in transactions and the equity value of consecutive transactions:

[0039] Q i,t =Q g,t +Q m,t (7)

[0040] Where: Q i,t is the operator’s comprehensive equity value; Q g,t The equity value obtained by the operator after each round of transactions; Q m,t is the equity value deducted by the operator after each round of continuous transactions, where Q i,t The lower limit is Upper limit is Q i,t The initial value of is 100;

[0041] The calculation formula for the equity value of participating in transactions and continuous transactions is:

[0042]

[0043]

[0044] Where θ n is the number of transactions the microgrid participates in at time t; n is the total number of transactions completed by all microgrids at time t, χ = 0.1;

[0045] After completing each round of transactions, the quotes of each node microgrid are updated by equations (10) and (11), and then enter the next round of transactions:

[0046]

[0047]

[0048] Where: ρ = 0.8, τ = 0.2; Q min With Q max are the equity values ​​of the microgrid with the lowest and highest equity coefficient values ​​at time t, respectively;

[0049] After all transactions are completed at time t, according to formula (9) m,t Update and calculate the comprehensive equity value Q i,t ; In the transaction at time t+1, the new Q i,t The value is the retained value after the transaction ends at time t-1.

[0050] The day-ahead scheduling plan is defined as:

[0051] Predict renewable energy power and load demand data for each time period of the next day on the previous day, and based on this data, arrange the power generation plan of each micro-power source in each time period of the next day according to certain economic criteria while meeting the load demand of the next day;

[0052] The single multi-energy microgrid system model constructed by the present invention includes:

[0053] (1) Energy input: including gas grid, heat station, distribution network, wind power (WP), photovoltaic (PV);

[0054] (2) Energy conversion equipment: including CCHP systems, electric refrigerators (ER), gas boilers (GB), fuel cells (FC), and electric boilers (EB);

[0055] (3) Energy storage equipment: energy storage equipment for four different energy forms: cold, heat and electricity.

[0056] Preferably, in step 6, the day-ahead optimal scheduling of the i multi-energy microgrid systems is an optimization problem with the goal of minimizing the total operating cost of the microgrid group, and its objective function is:

[0057]

[0058] Where: is the operating cost of the microgrid system; C loss Cost of curtailing wind and solar power in microgrids; For the multi-micro network market benefits;

[0059] The operating cost of the microgrid system is:

[0060]

[0061] Where: k is the total number of microgrid groups; are the operating costs of cooling, heating, electricity and gas systems of microgrid i, respectively. The specific expressions are:

[0062]

[0063] In the formula, α∈{WP,FC,PV,CCHP,EB,ER,EG,ES}, κ∈{ER,CCHP,CS}; ψ∈{FC,EG,CCHP,GB,GS}; They represent the unsatisfied electricity volume to be traded after each microgrid participates in market transactions, and are traded with the distribution network on demand; are the unit outputs of each system at time t; are the amount of heat and natural gas purchased by the multi-energy microgrid operator from the superior energy station at time t; are the operation and maintenance cost coefficients of the power, heat, cooling, and gas supply system units; H ,λ G are the external heat supply price and the external gas supply price respectively;

[0064] The total cost of curtailing wind and solar power in a microgrid group is:

[0065]

[0066] Where: P WP,t 、P PV,t are the output values ​​of wind and solar generators at time t respectively; is the predicted output value of the wind and solar power generator at time t; is the penalty coefficient for curtailing wind and solar power;

[0067] The revenue from microgrid market transactions is:

[0068] From the above formulas (4) and (5), we can know that the transaction volume of each microgrid market is and transaction price The total market transaction revenue of the microgrid group is composed of the product of the transaction volume of each microgrid and the corresponding transaction price during the entire transaction cycle, as shown in the following formula (16):

[0069]

[0070] The electric power balance equation is:

[0071]

[0072] Where, is the output of the equipment in the set α1∈{WP,FC,PV,CCHP}; is the output of the equipment in the set α2∈{EB,ER,EG}; P cha,t、P dis,t are the charging and discharging power of the battery respectively; P load,t is the microgrid electrical load;

[0073] The thermal power balance equation is:

[0074]

[0075] Where, For collection The output thermal power of the equipment; H buy,t For micro-online purchase of calories; H cha,t 、H dis,t are the heat storage and heat release power of the heat storage tank respectively; H load,t is the microgrid heat load;

[0076] The cooling load balance equation is:

[0077]

[0078] Where, is the cold conversion power of the equipment in the set κ1∈{ER,CCHP}; U cha (t), U dis (t) are the storage and release powers of the cold storage tank respectively; U load (t) is the cooling load of the microgrid;

[0079] The gas load balance equation is:

[0080]

[0081] Where G buy,t G EG,t is the gas supply of EG; is the gas consumption of the equipment in the set ψ1∈{FC,CCHP,GB}; G cha,t , G dis,t G is the amount of gas filling and releasing in the gas tank; load,t For gas load;

[0082] The coupled device model constraints are:

[0083]

[0084]

[0085] H GB,t =η GB G GB,t LHV (23)

[0086] U ER,t =P ER,t η ER (twenty four)

[0087] H EB,t =μ EB,t η EB P EB,t (25)

[0088]

[0089] Where μ CCHP,t 、μ FC,t It is the start-stop binary variable of CCHP unit and fuel cell, 1 means start-up and 0 means stop-up; is the conversion efficiency and heat loss rate of CCHP unit for cooling, heating and electricity; LHV is the lower heating value of natural gas, which is 9.7 (kW·h) / (N·m3); H WHB,t is the heating power of the waste heat boiler; η FC is the power generation efficiency of FC; η GB is the thermal conversion efficiency of GB; η ER is the energy efficiency coefficient of ER, η ER =3.2; μ EB,t is the start / stop state of EB; η EB is the thermal conversion efficiency of EB; η EG is the gas conversion efficiency of EG;

[0090] The energy storage device model and its upper and lower capacity limits are:

[0091]

[0092] Where: n∈{ES,HS,CS,GS}, E n,t is the storage amount of four different forms of energy in device n at time t; is the charging and discharging power of energy storage device n at time t; σ n is the energy self-loss rate of device n; are the energy charging and discharging efficiencies of device n, respectively; is the energy charge and discharge rate extreme value of device n; is the upper and lower limits of the load state of equipment n; C n is the rated energy storage capacity of equipment n; E n,t＝0 、E n,t＝24 The energy storage at 0:00 and 24:00 in a day;

[0093] The upper and lower limits of equipment output are:

[0094] μ n,t ·P min,n ≤P n,t ≤μ n,t ·P max,n (28)

[0095] Where: P n,t is the output value of device n; P max,n 、P min,n is the upper and lower limits of the output of device n; μ n,t Indicates the start / stop status of device n;

[0096] The unit climbing constraint is:

[0097] -R n,d ≤P n,t -P n,t-1 ≤R n,u (29)

[0098] Where: Where: R n,u 、R n,d is the up and down ramp power limit of controllable unit n;

[0099] The maximum transmission power constraint of the tie line is:

[0100]

[0101] Where: Provides safe transmission power limits between the microgrid and the main power grid;

[0102] The start and stop time constraints are:

[0103]

[0104] Where: T on 、T off On is the duration of on / off; min 、off min The maximum and minimum power on / off time are both 3 hours;

[0105] The solution is:

[0106] The scheduling model is solved by the 0-1 mixed integer linear programming method, and its general form is as follows:

[0107]

[0108] Where x is the optimization variable, including the unit output, the charging and discharging power of the energy storage, the power purchase amount of each microgrid, and the input of the energy conversion equipment; the equality constraints are the power balance constraints of the four energy forms and the energy storage constraints; and the inequality constraints are the unit operation constraints.

[0109] The present invention provides a multi-energy microgrid group transaction mechanism and a day-ahead optimization scheduling method based on blockchain technology. By combining blockchain technology with a multi-round transaction mechanism, a multi-energy microgrid day-ahead optimization scheduling model containing a market transaction plan is constructed, which has the following beneficial effects: (1) Compared with traditional centralized transactions, multi-energy microgrids participating in blockchain market transactions can not only trace transaction information to improve the openness and transparency of transactions, but also the multi-round transaction mechanism can ensure that each microgrid can obtain the maximum transaction income at each moment, thereby reducing the microgrid operation cost; (2) By adding the equity coefficient to the multi-energy microgrid market transaction, the interests of different microgrids are protected, and the benefits of each microgrid are made more balanced by affecting the quotation, thereby reducing the total operation cost of the microgrid group and improving the market transaction activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] The present invention will be further described below with reference to the accompanying drawings and examples:

[0111] Figure 1 This is a diagram of the multi-energy microgrid transaction framework based on blockchain in the present invention;

[0112] Figure 2 This is a flow chart of the matching process of the multi-round transaction mechanism of the present invention;

[0113] Figure 3 This is the smart contract transaction flow chart of the present invention;

[0114] Figure 4 This is a structural diagram of the multi-energy microgrid system of the present invention;

[0115] Figure 5 This is the time-of-use electricity price diagram of the microgrid of the present invention;

[0116] Figure 6 This is a unit output plan diagram when the microgrid 1 of the present invention participates in multi-microgrid transactions;

[0117] Figure 7 This is a unit output plan diagram when microgrid 2 of the present invention participates in multi-microgrid transactions;

[0118] Figure 8 This is a unit output plan diagram when the microgrid 3 of the present invention participates in multi-microgrid transactions;

[0119] Figure 9 This is a unit output plan diagram when the microgrid 4 of the present invention participates in multi-microgrid transactions;

[0120] Figure 10 This is a diagram showing the total renewable energy consumption of a multi-energy microgrid group under different calculation examples of the present invention;

[0121] Figure 11 This is a curve chart showing the transaction price and transaction volume changes in the Case 2 microgrid of the present invention;

[0122] Figure 12 This is a curve chart of transaction price and transaction volume changes in Case 3 microgrid of the present invention. DETAILED DESCRIPTION

[0123] like Figure 1-4 As shown, a multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology is characterized by including the following steps:

[0124] Step 1: Build a decentralized multi-energy microgrid transaction platform based on blockchain technology. Through P2P networks, smart contracts, and distributed storage technologies, it can effectively provide an effective solution to the privacy issues of information during transactions.

[0125] Step 2: Build a microgrid group transaction mechanism based on smart contracts, which is applicable to the multi-round two-way transaction mechanism of each microgrid operator to coordinate the interests of multiple parties in market transactions;

[0126] Step 3: In the day-ahead dispatch phase, each microgrid node formulates a day-ahead dispatch plan based on its own production capacity and load demand, uploads surplus and shortage power data to the smart contract, and obtains the quote of each microgrid according to the preset quote formula;

[0127] Step 4: After each microgrid has submitted its bid, the bids of the buyer and seller microgrids are ranked accordingly according to the sequence preset in the smart contract and the best buyer and seller are selected;

[0128] Step 5: To balance the interests of different nodes and prevent malicious competition such as price monopoly, the benefits between microgrids are balanced by constructing equity coefficients.

[0129] Step 6: Based on the characteristics of market transactions and dispatch, a multi-energy microgrid day-ahead optimal dispatch model with transactions is established;

[0130] Step 7: After completing the matching transaction, both parties use the multi-energy microgrid day-ahead optimization scheduling model to update the day-ahead scheduling plan according to the transaction plan, and perform simulation verification based on the multi-energy microgrid day-ahead scheduling model.

[0131] The framework of the decentralized multi-energy microgrid group transaction platform is:

[0132] (1) The decentralized operation mode of the P2P network can form a multi-party co-governance network by treating each microgrid operator as an independent node. Each microgrid node acts as both a server and a client, and can enter and exit the blockchain network at any time during the transaction time. The multi-energy microgrid group transaction process uses a decentralized client, which can effectively avoid network attacks and ensure the decentralization and security of the transaction system;

[0133] (2) Through distributed storage technology, each microgrid operator at an independent node stores complete blockchain data, including each microgrid operator's ID, energy type, transaction amount, transaction power, etc. When data is lost, it can be restored through data from other nodes, effectively preventing the problem of data loss and irrecoverable data in multi-microgrid transactions;

[0134] (3) Smart contracts are programs on the blockchain with preset rules and trigger conditions that allow nodes to complete transactions without a third party. Each microgrid node executes the transaction mechanism function embedded in the smart contract by submitting a transaction request, triggering state changes through preset responses, and writing the process to the blockchain for storage.

[0135] The microgrid group transaction mechanism based on smart contracts is:

[0136] In a multi-round two-way trading mechanism, each market participant can be both a buyer and a seller based on their own production capacity, freely conducting many-to-many transactions, with buyers and sellers optimally matched based on their bids. In this trading mechanism, once a price is matched (instant transactions are completed through smart contracts), there is no need to consider the impact of bid time, and buyers and sellers are ranked and matched based on the principle of "highest bidder wins";

[0137] The transaction matching process of the multi-round transaction mechanism is as follows:

[0138] (1) The smart contract divides each microgrid into buyers and sellers based on the amount of electricity to be traded input by each microgrid and formulates differentiated quotations based on the preset quotation formula;

[0139] (2) In the transaction matching process, the buyer with the highest bid and the seller with the lowest bid in each round are regarded as the best transaction parties, the transaction volume is the party with less volume to be traded, and the average of the two bids is taken as the transaction price of this round of transactions;

[0140] (3) After each round of transactions is completed, the smart contract recalculates the quotation and the equity coefficient value based on the remaining electricity to be traded and enters the next round of transactions. When there is no remaining electricity to be sold or purchased at time t, the transaction will proceed to time t+1;

[0141] Preferably, the preset quotation formula in step 3 is:

[0142]

[0143] Where: μ is the microgrid preference parameter, which is set to 0.5; λ Buy,min ,λ Sell,min is the minimum price of electricity purchased or sold by each microgrid to the grid; Buy,t ,λ Sell,t is the electricity price purchased from the distribution network at time t; Purchase and sell electricity from the higher-level power grid for both buyer and seller microgrids; It is the microgrid electricity load demand of both buyers and sellers.

[0144] Preferably, the method for selecting the best buyer and seller is:

[0145] After each microgrid quotes, the prices of the buyer microgrid queue j∈B={1,2,L} ​​are arranged from high to low according to formula (2), and the prices of the seller microgrid queue i∈S={1,2,L} ​​are arranged from low to high, and the buyer with the highest bid is selected. The buyer is J, the best buyer, and the seller has the lowest bid. The seller is the best seller I;

[0146]

[0147] Where: is the nth round of quotation at time t, when n=1,

[0148] After selecting the best two parties in this round, if the seller has the lowest bid Greater than or equal to the highest bid among buyers If the precondition (3) holds, the transaction between seller i and buyer j is successful, and the transaction price of each round of transaction is and transaction volume Calculated by formula (4) and formula (5) respectively:

[0149]

[0150]

[0151]

[0152] Where, are the electricity to be traded of the optimal buyer and seller respectively;

[0153] After each round of trading, the remaining electricity to be traded between the buyer and the seller is determined by the preset formula (6):

[0154]

[0155] In the formula, OR(A,B) means that if either A or B is 0 or both are 0, the output is 1, and a new round of trading begins at time t+1; otherwise, the output is 0, and the trading continues in round n+1 at time t.

[0156] Preferably, the equity coefficient is constructed as follows:

[0157] The equity coefficient is composed of the equity value of participating in transactions and the equity value of consecutive transactions:

[0158] Q i,t =Q g,t +Q m,t (7)

[0159] Where: Q i,t is the operator’s comprehensive equity value; Q g,t The equity value obtained by the operator after each round of transactions; Q m,t is the equity value deducted by the operator after each round of continuous transactions, where Q i,t The lower limit is Upper limit is Q i,t The initial value of is 100;

[0160] The calculation formula for the equity value of participating in transactions and continuous transactions is:

[0161]

[0162]

[0163] Where θ n is the number of transactions the microgrid participates in at time t; n is the total number of transactions completed by all microgrids at time t, χ = 0.1;

[0164] After completing each round of transactions, the quotes of each node microgrid are updated by equations (10) and (11), and then enter the next round of transactions:

[0165]

[0166]

[0167] Where: ρ = 0.8, τ = 0.2; Q min With Q max are the equity values ​​of the microgrid with the lowest and highest equity coefficient values ​​at time t, respectively;

[0168] After all transactions are completed at time t, according to formula (9) m,t Update and calculate the comprehensive equity value Q i,t ; In the transaction at time t+1, the new Q i,t The value is the retained value after the transaction ends at time t-1.

[0169] The day-ahead scheduling plan is defined as:

[0170] Predict renewable energy power and load demand data for each time period of the next day on the previous day, and based on this data, arrange the power generation plan of each micro-power source in each time period of the next day according to certain economic criteria while meeting the load demand of the next day;

[0171] The single multi-energy microgrid system model constructed by the present invention includes:

[0172] (1) Energy input: including gas grid, heat station, distribution network, wind power (WP), photovoltaic (PV);

[0173] (2) Energy conversion equipment: including CCHP systems, electric refrigerators (ER), gas boilers (GB), fuel cells (FC), and electric boilers (EB);

[0174] (3) Energy storage equipment: energy storage equipment for four different energy forms: cold, heat and electricity.

[0175] Preferably, in step 6, the day-ahead optimal scheduling of the i multi-energy microgrid systems is an optimization problem with the goal of minimizing the total operating cost of the microgrid group, and its objective function is:

[0176]

[0177] Where: is the operating cost of the microgrid system; C loss Cost of curtailing wind and solar power in microgrids; For the multi-micro network market benefits;

[0178] The operating cost of the microgrid system is:

[0179]

[0180] Where: k is the total number of microgrid groups; are the operating costs of cooling, heating, electricity and gas systems of microgrid i, respectively. The specific expressions are:

[0181]

[0182] In the formula, α∈{WP,FC,PV,CCHP,EB,ER,EG,ES}, κ∈{ER,CCHP,CS}; ψ∈{FC,EG,CCHP,GB,GS}; They represent the unsatisfied electricity volume to be traded after each microgrid participates in market transactions, and are traded with the distribution network on demand; are the unit outputs of each system at time t; are the amount of heat and natural gas purchased by the multi-energy microgrid operator from the superior energy station at time t; are the operation and maintenance cost coefficients of the power, heat, cooling, and gas supply system units; H ,λ G are the external heat supply price and the external gas supply price respectively;

[0183] The total cost of curtailing wind and solar power in a microgrid group is:

[0184]

[0185] Where: P WP,t 、P PV,t are the output values ​​of wind and solar generators at time t respectively; is the predicted output value of the wind and solar power generator at time t; is the penalty coefficient for curtailing wind and solar power;

[0186] The revenue from microgrid market transactions is:

[0187] From the above formulas (4) and (5), we can know that the transaction volume of each microgrid market is and transaction price The total market transaction revenue of the microgrid group is composed of the product of the transaction volume of each microgrid and the corresponding transaction price during the entire transaction cycle, as shown in the following formula (16):

[0188]

[0189] The electric power balance equation is:

[0190]

[0191] Where, is the output of the equipment in the set α1∈{WP,FC,PV,CCHP}; is the output of the equipment in the set α2∈{EB,ER,EG}; P cha,t 、P dis,t are the charging and discharging power of the battery respectively; P load,t is the microgrid electrical load;

[0192] The thermal power balance equation is:

[0193]

[0194] Where, For collection The output thermal power of the equipment; H buy,t For micro-online purchase of calories; H cha,t 、H dis,t are the heat storage and heat release power of the heat storage tank respectively; H load,t is the microgrid heat load;

[0195] The cooling load balance equation is:

[0196]

[0197] Where, is the cold conversion power of the equipment in the set κ1∈{ER,CCHP}; U cha (t), U dis (t) are the storage and release powers of the cold storage tank respectively; U load (t) is the cooling load of the microgrid;

[0198] The gas load balance equation is:

[0199]

[0200] Where G buy,t G EG,t is the gas supply of EG; is the gas consumption of the equipment in the set ψ1∈{FC,CCHP,GB}; G cha,t , G dis,t G is the amount of gas filling and releasing in the gas tank; load,t For gas load;

[0201] The coupled device model constraints are:

[0202]

[0203]

[0204] H GB,t =η GB G GB,t LHV (23)

[0205] U ER,t =P ER,t η ER (twenty four)

[0206] H EB,t =μ EB,t η EB P EB,t (25)

[0207]

[0208] Where μ CCHP,t 、μ FC,t It is the start-stop binary variable of CCHP unit and fuel cell, 1 means start-up and 0 means stop-up; is the conversion efficiency and heat loss rate of CCHP unit for cooling, heating and electricity; LHV is the lower heating value of natural gas, which is 9.7 (kW·h) / (N·m3); H WHB,t is the heating power of the waste heat boiler; η FCis the power generation efficiency of FC; η GB is the thermal conversion efficiency of GB; η ER is the energy efficiency coefficient of ER, η ER =3.2; μ EB,t is the start / stop state of EB; η EB is the thermal conversion efficiency of EB; η EG is the gas conversion efficiency of EG;

[0209] The energy storage device model and its upper and lower capacity limits are:

[0210]

[0211] Where: n∈{ES,HS,CS,GS}, E n,t is the storage amount of four different forms of energy in device n at time t; is the charging and discharging power of energy storage device n at time t; σ n is the energy self-loss rate of device n; are the energy charging and discharging efficiencies of device n, respectively; is the energy charge and discharge rate extreme value of device n; is the upper and lower limits of the load state of equipment n; C n is the rated energy storage capacity of equipment n; E n,t＝0 、E n,t＝24 The energy storage at 0:00 and 24:00 in a day;

[0212] The upper and lower limits of equipment output are:

[0213] μ n,t ·P min,n ≤P n,t ≤μ n,t ·P max,n (28)

[0214] Where: P n,t is the output value of device n; P max,n 、P min,n is the upper and lower limits of the output of device n; μ n,t Indicates the start / stop status of device n;

[0215] The unit climbing constraint is:

[0216] -R n,d ≤P n,t -P n,t-1 ≤R n,u (29)

[0217] Where: Where: R n,u 、R n,d is the up and down ramp power limit of controllable unit n;

[0218] The maximum transmission power constraint of the tie line is:

[0219]

[0220] Where: Provides safe transmission power limits between the microgrid and the main power grid;

[0221] The start and stop time constraints are:

[0222]

[0223] Where: T on 、T off On is the duration of on / off; min 、off min The maximum and minimum power on / off time are both 3 hours;

[0224] The solution is:

[0225] The scheduling model is solved by the 0-1 mixed integer linear programming method, and its general form is as follows:

[0226]

[0227] Where x is the optimization variable, including the unit output, the charging and discharging power of the energy storage, the power purchase amount of each microgrid, and the input of the energy conversion equipment; the equality constraints are the power balance constraints of the four energy forms and the energy storage constraints; and the inequality constraints are the unit operation constraints.

[0228] This paper takes four multi-energy microgrid node systems in the blockchain microgrid trading market as the research object. The scheduling cycle is 24 hours, the interval is 1 hour, the heat purchase price of each microgrid is 0.6 yuan / (kW·h), and the gas purchase price is 2.28 yuan / m3. The time-of-use electricity price of the microgrid is as follows: Figure 5 shown.

[0229] To verify the effectiveness of the proposed trading mechanism, this paper compares the optimal scheduling results of multi-energy microgrids with three different trading strategies.

[0230] Case 1: Multi-energy microgrid optimal scheduling without considering the trading mechanism;

[0231] Case 2: Optimal scheduling of a multi-energy microgrid considering a trading mechanism without equity coefficients;

[0232] Case 3: Optimal scheduling of multi-energy microgrids considering both transaction mechanism and equity coefficient.

[0233] Table 1 shows the operating costs of each multi-energy microgrid under three different operating modes. Compared to Case 1, which operated independently, the total cost of the microgrid group participating in market transactions in Case 2 was 316.92 yuan lower. This is because each microgrid purchased electricity from the market at a price lower than the grid purchase price and sold it at a price higher than the grid price. In Case 3, after adding the equity factor, the costs of microgrids 1 and 3 decreased again by 27.59 yuan and 12.7 yuan, respectively, while the costs of microgrids 2 and 4 increased by 25.17 yuan and 15.12 yuan, respectively. This is because the introduction of the equity factor, which modifies the bids of each microgrid, changes the original transaction partners. This restricts microgrids that hold a monopoly position in the market, reducing their number of transactions. This results in a decrease in the costs of microgrids 1 and 3 and an increase in the costs of microgrids 2 and 4.

[0234] Table 1 Total operating costs under different examples

[0235]

[0236] The dispatch results of each microgrid in Case 3 are as follows: Figures 6 to 9 The output of each microgrid is mainly provided by fuel cells, CCHP units and electric boilers. Figure 6 According to China Micro Network 1, fuel cell output accounts for 21.98% of the total output, CCHP unit output accounts for 29.29% of the total output, and electric boiler output accounts for 20.58% of the total output; Figure 7 In China Microgrid 2, the output of fuel cells, CCHP units, and electric boilers all account for more than 20% of the total output; Figure 8 In China Microgrid 3, the combined output of fuel cells, CCHP units, and electric boilers accounts for more than 70% of the total output; Figure 9 In microgrid 4, the output of CCHP units and electric boilers each accounts for nearly 30% of the total output. It can be seen that the output of each microgrid can meet the supply and demand balance of cooling, heating, and electricity. The simulation results verify the effectiveness of the trading mechanism proposed in this paper for optimized scheduling.

[0237] Figure 10 Figure 2 shows the total renewable energy consumption of a multi-energy microgrid cluster under different calculation cases. It can be seen that the inter-microgrid trading mechanism can promote renewable energy consumption. In Case 1, when the microgrids do not participate in trading, the renewable energy consumption rate is 95.37% between 11:00 AM and 3:00 PM and 7:00 PM and 8:00 PM. In Case 2, when the microgrids participate in trading, the consumption rate is 98.43%, an increase of 3.14% compared to Case 1. In Case 3, when the trading partners are changed and the transaction price is lower, the renewable energy consumption rate reaches 100%.

[0238] The transaction volume and transaction price of each microgrid in Case 2 and Case 3 are as follows: Figures 11-12As shown in the figure. Among them, the transaction volume is positive, which means buying electricity, and negative, which means selling electricity. Figure 11 In the figure, no transactions occurred between 1:00-10:00 and 21:00-24:00. The corresponding relationships and times of the microgrids in the first round of transactions are (1-4, 11), (1-3, 12), (2-4, 13), (2-4, 14), (2-4, 15), (1-4, 19), (1-4, 20), and the corresponding relationships and times of the microgrids in the first round of transactions are (2-3, 11), (2-3, 19), (2-3, 20) (the number before the comma in the brackets indicates the microgrid that traded, and the number after the comma indicates the time of the transaction). It can be seen that the total number of transactions for microgrids 2 and 4 is 6 times each, and the total number of transactions for microgrids 1 and 3 is 4 times each. This is because microgrids 2 and 4 have more electricity to be traded, and their bids are more advantageous, so the number of transactions is greater. Microgrids 1 and 3 have less electricity to be traded, and their bids are not advantageous, so the number of transactions is even less. Figure 12 In the example, the equity coefficient changes the bids of each microgrid, resulting in the following transaction matching patterns: (1-4, 11), (2-3, 11), (1-3, 12), (2-3, 13), (1-4, 14), (1-3, 15), (1-4, 19), (2-3, 19), (1-4, 20), and (2-3, 20). Therefore, the total number of transactions for microgrids 2 and 4 becomes 4, while the total number of transactions for microgrids 1 and 3 becomes 6.

[0239] Table 2 Changes in equity coefficient

[0240]

[0241] Taking 13:00, 14:00 and 15:00 as examples, we analyze the effect of each microgrid’s equity coefficient value on the transformation of the transaction object, and then analyze its impact on the quotes of each microgrid. Figures 11-12It can be seen that at 13:00, the transaction object changes from (2-4, 13) to (2-3, 13). According to equations (10) and (11), the inclusion of the equity coefficient changes the price of microgrid 4 from 0.741 yuan to 0.753 yuan, and the price of microgrid 3 from 0.747 yuan to 0.742 yuan. Therefore, the price of microgrid 3 is more advantageous than that of microgrid 4, becoming the best selling price. Similarly, at 14:00, the transaction object changes from (2-4, 14) to (1-4, 14), the price of microgrid 2 changes from 0.760 yuan to 0.775 yuan, and the price of microgrid 1 changes from 0.766 yuan to 0.770 yuan, which is lower than the price of microgrid 2 and becomes the best selling price. At 15:00, the transaction object changed from (1-3, 15) to (2-4, 15), the price of microgrid 1 changed from 0.758 yuan to 0.763 yuan, while the price of microgrid 2 remained unchanged at 0.761 yuan, becoming the best selling price. The unchanged price of microgrid 4 was 0.729 yuan, exceeding the 0.738 yuan of microgrid 3 to become the best buying price.

[0242] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology, characterized in that: The following steps are involved: Step 1: Build a decentralized multi-energy microgrid transaction platform based on blockchain technology. Through P2P networks, smart contracts, and distributed storage technologies, it can effectively provide an effective solution to the privacy issues of information during transactions. Step 2: Build a microgrid group transaction mechanism based on smart contracts, which is applicable to the multi-round two-way transaction mechanism of each microgrid operator to coordinate the interests of multiple parties in market transactions; Step 3: In the day-ahead dispatch phase, each microgrid node formulates a day-ahead dispatch plan based on its own production capacity and load demand, uploads surplus and shortage power data to the smart contract, and obtains the quote of each microgrid according to the preset quote formula; Step 4: After each microgrid has submitted its bid, the bids of the buyer and seller microgrids are ranked accordingly according to the sequence preset in the smart contract and the best buyer and seller are selected; Step 5: To balance the interests of different nodes and prevent malicious competition such as price monopoly, the benefits between microgrids are balanced by constructing equity coefficients. Step 6: Based on the characteristics of market transactions and dispatch, a multi-energy microgrid day-ahead optimal dispatch model with transactions is established; Step 7: After completing the matching transaction, the two parties use the multi-energy microgrid day-ahead optimization scheduling model to update the day-ahead scheduling plan according to the transaction plan, and perform simulation verification based on the multi-energy microgrid day-ahead scheduling model; The method for constructing the equity coefficient is: The equity coefficient is composed of the equity value of participating in transactions and the equity value of consecutive transactions: ；(7) Where: is the operator’s comprehensive equity value; The equity value obtained by the operator after each round of transactions; is the equity value deducted by the operator after each round of continuous transactions, where The lower limit is , the upper limit is , The initial value of is 100; The calculation formula for the equity value of participating in transactions and continuous transactions is: ；(8) ；(9) Where, For microgrids The number of transactions participated in at any given moment; for The total number of transactions completed by all microgrids at the moment, ; After completing each round of transactions, the quotes of each node microgrid are updated by equations (10) and (11), and then enter the next round of transactions: ；(10) ；(11) Where: 、 ; and They are The equity value of the microgrid with the lowest and highest equity coefficient values ​​at the moment; exist After all transactions are completed, according to formula (9) Update and calculate comprehensive equity value ;exist In the transaction of time, new The value is The value is retained after the moment transaction ends; In step 6, the day-ahead optimal scheduling of the i-number multi-energy microgrid system is an optimization problem with the goal of minimizing the total operating cost of the microgrid group. Its objective function is: ；(12) Where: The operating cost of the microgrid system; Cost of curtailing wind and solar power in microgrids; For the multi-micro network market benefits; The operating cost of the microgrid system is: ；(13) Where: is the total number of microgrid groups; 、 、 、 Microgrid Operating costs of cooling, heating, electricity, and gas systems, specifically expressed as follows: ；(14) Where, , ; ; ; 、 They represent the unsatisfied electricity volume to be traded after each microgrid participates in market transactions, and are traded with the distribution network on demand; 、 、 、 For each system The unit output at the moment; 、 They are The amount of heat and natural gas purchased by the multi-energy microgrid operator from the upper energy station at any given moment; 、 、 、 are the operation and maintenance cost coefficients of the power, heat, cooling and gas supply system units output respectively; 、 are the external heat supply price and the external gas supply price respectively; The total cost of curtailing wind and solar power in a microgrid group is: ；（15） Where: 、 Wind and solar units Output value at any moment; 、 For wind and solar units The predicted output value at the time; 、 is the penalty coefficient for curtailing wind and solar power; The revenue from microgrid market transactions is: The total market transaction revenue of the microgrid group is composed of the product of the transaction volume of each microgrid and the corresponding transaction price during the entire transaction cycle, as shown in the following formula (16): ；（16） The electric power balance equation is: ；（17） Where, For collection The output of the equipment; For collection The output of the equipment; 、 are the charge and discharge power of the battery respectively; is the microgrid electrical load; The thermal power balance equation is: ；（18） Where, For collection The output thermal power of the equipment; Buy calories for micro-net; 、 are the heat storage and release power of the heat storage tank respectively; is the microgrid heat load; The cooling load balance equation is: ；（19） Where, For collection Cold conversion power of medium equipment; 、 They are the storage and release powers of the cold storage tank respectively; It is the cooling load of the microgrid; The gas load balance equation is: ；（20） Where, Purchase gas for the micro-net; is the gas supply of EG; For collection Gas consumption of medium equipment; 、 Fill and release the gas tank; For gas load; The coupled device model constraints are: ；（21） ；（22） ；（23） ；（24） ；（25） ；（26） Where, 、 It is the start-stop binary variable of CCHP unit and fuel cell, 1 means start-up and 0 means stop-up; 、 、 、 The conversion efficiency and heat loss rate of CCHP units for cooling, heating and electricity; is the lower calorific value of natural gas, which is 9.7 (kW·h) / (N·m3); The heating power of the waste heat boiler; is the power generation efficiency of FC; is the thermal conversion efficiency of GB; is the energy efficiency coefficient of ER, ; The start and stop status of EB; is the thermal conversion efficiency of EB; is the gas conversion efficiency of EG; The energy storage device model and its upper and lower capacity limits are: ；（27） Where: 、 Four different forms of energy Time equipment Storage capacity in 、 For energy storage equipment exist Charging and discharging energy at all times; For equipment Energy self-loss rate; 、 Equipment Energy charging and discharging efficiency; 、 For equipment The energy charge and discharge rate extremes; 、 For equipment Upper and lower limits of load state; For equipment Rated energy storage capacity; 、 The energy storage at 0:00 and 24:00 in a day; The upper and lower limits of equipment output are: ；（28） Where: For equipment Output value; 、 For equipment Output upper and lower limits; Representation device Start and stop status; The unit climbing constraint is: ；（29） In the formula: In the formula: 、 For controllable units Up and down climbing power limit; The maximum transmission power constraint of the tie line is: ；（30） Where: 、 Provides safe transmission power limits between the microgrid and the main power grid; The start and stop time constraints are: ；（31） Where: 、 They are the continuous on / off time; 、 The maximum and minimum power on / off time are both 3 hours; The solution is: The scheduling model is solved by the 0-1 mixed integer linear programming method, and its general form is as follows: ；（32） Where: The optimization variables include unit output, energy storage charging and discharging power, power purchase amount of each microgrid, and input of energy conversion equipment; the equality constraints are power balance constraints of four energy forms and energy storage constraints; the inequality constraints are unit operation constraints.

2. According to claim 1, a multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology is characterized in that: The preset quotation formula in step 3 is: ； (1) Where: is the microgrid preference parameter, which is set to 0.5; 、 The minimum price for each microgrid to purchase and sell electricity from the grid; 、 for Always buy and sell electricity from the distribution network at the same price; 、 Purchase and sell electricity from the higher-level power grid for both buyer and seller microgrids; 、 It is the microgrid electricity load demand of both buyers and sellers.

3. The multi-energy microgrid group transaction mechanism and day-ahead optimization scheduling method based on blockchain technology according to claim 1 is characterized in that: The method for selecting the best buyer and seller is: After each microgrid quotes, the buyer microgrid queue is calculated by formula (2): The prices are arranged from high to low, and the seller's microgrid queue is Arrange the prices from low to high and select the buyer with the highest bid The buyer of The seller has the lowest bid The seller is the best seller ; ； (2) Where: 、 for Moment Round quotation, hour, , ; After selecting the best two parties in this round, if the seller has the lowest bid Greater than or equal to the highest bid among buyers , the precondition (3) is established, then the seller With the buyer The transaction is successful, the transaction price of each round of transaction and transaction volume Calculated by formula (4) and formula (5) respectively: ；(3) ；(4) ； (5) Where, 、 are the electricity to be traded of the optimal buyer and seller respectively; After each round of trading, the remaining electricity to be traded between the buyer and the seller is determined by the preset formula (6): ； (6) Where, Indicates that when either A or B is 0 or both are 0, output 1 and enter A new round of transactions starts at the moment; otherwise, output 0 and enter Round continues Trade at all times.

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