A multi-energy micro-grid point-to-point energy scheduling method, device, equipment and medium

Abstract

The application discloses a kind of multi-energy microgrid point-to-point energy scheduling method, device, equipment and medium, the method includes: according to the network coupling constraint of energy microgrid, equipment characteristic constraint and scheduling operation constraint, constructs and contains the multi-energy microgrid optimization model of electricity, gas, heat energy;Using alternating direction multiplier algorithm, the distributed iteration solution of multi-energy microgrid optimization model is carried out, and the P2P energy exchange amount between each microgrid is obtained;Based on P2P energy exchange amount, the P2P energy exchange price is calculated using Rubinstein bargaining solution RBS strategy, and the incentive signal of microgrid energy exchange is obtained;According to P2P energy exchange amount and incentive signal, the comprehensive scheduling scheme of microgrid is obtained.The application can carry out multi-energy cooperation, guarantee information security, improve interaction fairness and resource utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of microgrid optimization, and in particular to a multi-energy microgrid P2P (Peer-to-Peer) energy scheduling method, device, equipment and medium. BACKGROUND

[0002] With the rapid development of renewable energy and multi-energy complementary technology, building a flexible and efficient multi-energy microgrid system has become a key way to improve energy utilization efficiency and system resilience. Multi-energy microgrid has the ability of local power generation, combined heat and power, energy storage regulation, etc., and can realize self-balancing and self-regulation of regional energy. Due to the uncertainty of renewable energy and the diversity of microgrid operation goals, there is a mismatch between resource supply and demand in different microgrids, which easily causes local energy abandonment and global efficiency decline.

[0003] In order to improve the overall performance of the system, some existing technologies use P2P energy exchange mechanism for energy scheduling, which allows energy exchange between microgrids based on negotiation, effectively improving the resource coordination ability of the system. However, the existing technology has privacy and security problems in the process of energy interaction, and the satisfaction of the energy interaction participants needs to be improved. SUMMARY

[0004] To solve the above technical problems, the present application provides a multi-energy microgrid P2P energy scheduling method, device, equipment and medium, which can solve the problems of lack of multi-energy coordination, privacy protection and interaction fairness in multi-energy microgrid P2P energy scheduling, and improve the resource utilization rate.

[0005] The present application provides a multi-energy microgrid P2P energy scheduling method, which comprises:

[0006] According to the network coupling constraint, device characteristic constraint and scheduling operation constraint of the energy microgrid, a multi-energy microgrid optimization model containing electric, gas and heat energy is constructed;

[0007] The alternating direction multiplier algorithm is used to distribute and iteratively solve the multi-energy microgrid optimization model, and the P2P energy exchange amount between each microgrid is obtained;

[0008] Based on the P2P energy exchange amount, the Rubinstein bargaining solution RBS strategy is used to calculate the P2P energy exchange price, and the incentive signal of microgrid energy exchange is obtained;

[0009] According to the P2P energy exchange amount and the incentive signal, the comprehensive scheduling scheme of the microgrid is obtained.

[0010] As an improvement of the above scheme, the network coupling constraint includes: power grid interaction constraint, gas network interaction constraint;

[0011] The device characteristic constraints include: electric energy storage constraints, electric-gas conversion device constraints, combined heat and power device constraints, and gas boiler constraints.

[0012] The dispatch operation constraints include: demand response constraints, energy balance constraints, and microgrid operation cost constraints.

[0013] As an improvement of the above scheme, the alternating direction multiplier algorithm is used to solve the multi-energy microgrid optimization model in a distributed manner to obtain point-to-point (P2P) energy exchange between microgrids, including:

[0014] Based on the multi-energy microgrid optimization model, a clearing optimization structure considering P2P energy exchange is constructed according to the P2P energy flow constraints between microgrids, and an augmented Lagrangian function is constructed according to the clearing optimization structure.

[0015] The alternating direction multiplier algorithm is used to solve the augmented Lagrangian function iteratively to obtain the P2P energy exchange between microgrids.

[0016] As an improvement of the above scheme, the clearing optimization structure introduces auxiliary variables in the P2P energy flow constraints.

[0017] The alternating direction multiplier algorithm is used to solve the augmented Lagrangian function iteratively, including:

[0018] Initialize auxiliary variables and dual variables.

[0019] Based on the auxiliary variables and the dual variables, update the decision variables of each microgrid and the P2P energy exchange according to the augmented Lagrangian function.

[0020] Update the auxiliary variables according to the current dual variables and P2P energy exchange.

[0021] Update the dual variables according to the current auxiliary variables and P2P energy exchange.

[0022] Calculate the original residual according to the current P2P energy exchange and auxiliary variables.

[0023] Determine whether the original residual is less than a preset tolerance, if yes, output the current P2P energy exchange, if not, repeat the updating steps of decision variables, P2P energy exchange, auxiliary variables and dual variables until the original residual is less than the preset tolerance, and output the current P2P energy exchange.

[0024] As an improvement of the above scheme, based on the P2P energy exchange, the Rubinstein bargaining solution (RBS) strategy is used to calculate the P2P energy exchange price to obtain the incentive signal of microgrid energy exchange, including:

[0025] Construct an affine utility function that satisfies the RBS negotiation conditions based on the RBS strategy;

[0026] The affine utility function is normalized to obtain a normalized utility function, which quantifies participants' satisfaction with prices.

[0027] Based on the normalized utility function and combined with the maximization of utility under the RBS strategy, the P2P energy exchange price under the global optimal solution of RBS is calculated to obtain the initial price.

[0028] An energy sharing priority coefficient is constructed based on the P2P energy exchange volume, and the final P2P energy exchange price is calculated by multiplying the initial price by the energy sharing priority coefficient.

[0029] As an improvement to the above scheme, the affine utility function includes:

[0030]

[0031]

[0032] in, , Let i and j be the affine utility functions of the participating micronets, respectively. , The opportunity cost per unit for purchasing / selling energy of micronets i and j, respectively, is based on the mainnet. , These represent the P2P energy exchange volumes of microgrids i and j, respectively. , These represent the proportions of energy exchanged to their respective total demand and total surplus. The unit price of energy in P2P , These are the energy transmission cost coefficients for microgrids i and j, respectively.

[0033] As an improvement to the above scheme, the step of calculating the P2P energy exchange price under the global optimum condition of RBS, based on the normalized utility function and combined with maximizing utility under the RBS strategy, includes:

[0034] Construct a correlation model between the normalized utility function and the energy exchange price:

[0035]

[0036] in, , These are the normalized utility metrics for microgrids i and j, respectively. , The opportunity cost per unit for purchasing / selling energy of micronets i and j, respectively, is based on the mainnet. , These represent the P2P energy exchange volumes of microgrids i and j, respectively. , These represent the proportions of energy exchanged to their respective total demand and total surplus. The unit price of energy in P2P , These are the energy transmission cost coefficients for microgrids i and j, respectively. , These represent the maximum permissible proportions of P2P energy exchange volume for microgrids i and j at time t to their respective total demand and total surplus.

[0037] Using maximizing utility under the RBS strategy as the objective function:

[0038]

[0039] in, , ;

[0040] The condition for the global optimal solution of RBS is: ;

[0041] Calculate the P2P energy exchange price under the condition of the global optimum of RBS:

[0042]

[0043] in, Let be the P2P energy exchange price of microgrids i and j at time t, derived based on the RBS strategy.

[0044] This invention also provides a multi-energy microgrid point-to-point energy dispatching device, comprising:

[0045] The microgrid model construction module is used to construct a multi-energy microgrid optimization model that includes electricity, gas, and heat energy based on the network coupling constraints, equipment characteristic constraints, and scheduling operation constraints of the energy microgrid.

[0046] The energy exchange calculation module is used to perform distributed iterative solution of the multi-energy microgrid optimization model using the alternating direction multiplier algorithm to obtain the point-to-point P2P energy exchange between each microgrid.

[0047] The energy exchange pricing module is used to calculate the P2P energy exchange price based on the P2P energy exchange volume using the Rubinstein bargaining solution (RBS) strategy, and obtain the incentive signal for microgrid energy exchange.

[0048] The microgrid energy scheduling module is used to obtain a comprehensive scheduling scheme for the microgrid based on the P2P energy exchange volume and the excitation signal.

[0049] The embodiment of the present application further provides a computer device comprising a processor and a memory, the memory storing a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to realize the multi-energy micro-grid point-to-point energy scheduling method.

[0050] The embodiment of the present application further provides a computer readable storage medium storing a computer program, wherein the computer program controls the device where the computer readable storage medium is located to execute the multi-energy micro-grid point-to-point energy scheduling method when the computer program is running.

[0051] Compared with the prior art, the embodiment of the present application provides a multi-energy micro-grid point-to-point energy scheduling method, device, equipment and medium, which has the following advantages: by constructing a multi-energy micro-grid optimization model containing electric, gas and heat energy according to the network coupling constraint, device characteristic constraint and scheduling operation constraint of the energy micro-grid, multi-energy collaboration can be performed, which is beneficial to resource scheduling; the multi-energy micro-grid optimization model is distributedly iteratively solved by using an alternating direction multiplier algorithm to obtain point-to-point P2P energy exchange amount between micro-grids, only limited necessary data needs to be exchanged, the risk of sensitive information leakage is significantly reduced, and the feasibility and convergence of energy distribution are also ensured; based on the P2P energy exchange amount, a Rubinstein bargaining solution RBS strategy is used to calculate P2P energy exchange price to obtain an incentive signal of micro-grid energy exchange, which improves the fairness of energy interaction of each micro-grid, is beneficial to enhancing the collaboration and robustness of the whole system, and improves resource utilization rate; and according to the P2P energy exchange amount and the incentive signal, a comprehensive scheduling scheme of the micro-grid is obtained, which solves the problems of lack of multi-energy collaboration, fair pricing and privacy protection in the existing multi-energy micro-grid P2P energy interaction. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is a flowchart of a multi-energy micro-grid point-to-point energy scheduling method provided by the embodiment of the present application;

[0053] Figure 2 is a micro-grid overall system structure diagram provided by the embodiment of the present application;

[0054] Figure 3 is an electric, gas and heat load diagram of each micro-grid provided by the embodiment of the present application;

[0055] Figure 4 is an electric and gas P2P energy exchange diagram of each micro-grid provided by the embodiment of the present application;

[0056] Figure 5 is a state diagram of electric and gas purchase and sale interaction of each micro-grid provided by the embodiment of the present application;

[0057] Figure 6The following are typical moment-by-moment P2P energy flow diagrams for each microgrid provided in the embodiments of the present invention;

[0058] Figure 7 Cost iteration diagrams for each microgrid provided in embodiments of the present invention;

[0059] Figure 8 This is a schematic diagram of the structure of a multi-energy microgrid point-to-point energy dispatching device provided in an embodiment of the present invention;

[0060] Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1 , Figure 1 This is a flowchart illustrating a point-to-point energy dispatching method for multi-energy microgrids provided in an embodiment of the present invention. The point-to-point energy dispatching method for multi-energy microgrids includes:

[0063] S1: Based on the network coupling constraints, equipment characteristic constraints, and scheduling operation constraints of the energy microgrid, construct a multi-energy microgrid optimization model that includes electricity, gas, and heat energy;

[0064] As one optional embodiment, the network coupling constraints include: power grid interaction constraints and gas grid interaction constraints;

[0065] The equipment characteristic constraints include: electric energy storage constraints, electric-to-gas equipment constraints, combined heat and power equipment constraints, and gas boiler constraints.

[0066] The scheduling and operation constraints include: demand response constraints, energy balance constraints, and microgrid operating cost constraints.

[0067] Specifically, grid interaction constraints: microgrids employ DC power flow constraints to facilitate model solving. Current microgrid The grid interaction constraints with the main grid are as follows:

[0068]

[0069]

[0070] in, , represents the electricity purchase and sale amount, , represents the electricity purchase upper and lower limits, respectively, , represents the electricity sale upper and lower limits, respectively.

[0071] Gas network interaction constraint: Since the natural gas microgrid in reality mostly only purchases gas from the main gas network, this embodiment sets that it only interacts with the main gas network for gas purchase. The gas network interaction constraint between the natural gas microgrid and the main gas network at time t is as follows:

[0072]

[0073] wherein, represents the gas purchase amount, , represents the gas purchase upper and lower limits, respectively.

[0074] Electricity storage constraint: The storage charges when the load is low and discharges when the peak is high to stabilize the electricity by smoothing the intermittent wind power. The operation constraint condition of the storage system of the microgrid i at the time period t is modeled and analyzed, and the specific formula is as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] wherein, characterizes the state of charge of the energy storage unit, and the charging and discharging power is defined as , , the corresponding power threshold constraint is defined by , , and , represents the energy conversion efficiency coefficient of the charging and discharging link, respectively. characterizes the maximum rated capacity of the energy storage device, as a discharge depth parameter to constrain the system operation range.

[0082] The mathematical relationship between the energy storage capacity and the discharge depth is established by the formula

[0083] The formula​​​ With For the working state exclusive characteristics of energy storage devices, binary logic variables and constant M are introduced to construct mixed integer constraints, which strictly guarantee that charging and discharging processes do not overlap in time domain from a mathematical level.

[0084] Electro-gas equipment constraints: a mathematical model is established for the operation boundary of the electro-gas equipment of microgrid i at time period t:

[0085]

[0086]

[0087] wherein, is the electro-gas gas production, is the electro-gas power consumption, is the energy conversion coefficient of electro-gas, is the conversion efficiency, is the high calorific value of natural gas, , is the upper and lower limit of electro-gas power consumption, the first formula indicates that the electro-gas equipment converts excess renewable energy into fuel gas through 45%-60% energy conversion efficiency, and the second formula limits the operation range of the equipment.

[0088] Combined heat and power equipment constraints: CHP equipment constraints, the CHP unit delivers heat and electricity to the regional heating network and the microgrid through the consumption of natural gas, and the operation restrictions that the CHP in microgrid i needs to meet at time period t include:

[0089]

[0090]

[0091] wherein, the first formula indicates the natural gas supply required for the CHP operation to produce heat and generate electricity, , , respectively represent the natural gas consumption, power output and heat output of the CHP unit; , respectively are the minimum and maximum values of CHP power output, , respectively are the minimum and maximum values of CHP heat output, is the conversion efficiency of the combined heat and power unit, is the high calorific value of natural gas.

[0092] Gas boiler constraints: Gas boiler utilizes natural gas as fuel to provide thermal energy, which is considered as an environmentally friendly clean energy. The heat production constraints of gas boiler in microgrid i within time period t are shown as follows:

[0093]

[0094]

[0095] where, is the thermal power output of gas boiler, is the natural gas consumption, represents the conversion efficiency coefficient of natural gas to thermal energy of gas boiler, , is the upper and lower limits of natural gas consumption.

[0096] Demand response constraints: Multi-energy microgrid integrates diversified energy demands, and in order to improve the utilization efficiency of demand side resources, three main demand response (DR) modes are considered, namely, load shedding, time shifting load and load conversion. The demand response related constraints of microgrid i within time period t are shown as follows:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] where, the first formula is the energy load balance equation formed after considering the above DR strategy, represents the energy carrier type ( is electricity, is natural gas), and ; , are the energy loads before and after DR execution, respectively; , , , represent the energy load reduction, upward and downward shift of energy demand, and conversion of energy load, respectively; , , respectively represent the upper limit of load reduction, the upper limit of load shifting up / down, and the upper and lower limits of load conversion amount; is the conversion efficiency factor.

[0105] Energy balance constraint: the balance relationship of electricity, gas, and heat energy forms within the microgrid i in period t is shown as follows:

[0106]

[0107]

[0108]

[0109] wherein, , and respectively represent the wind power and photovoltaic power in renewable energy, , represent the electricity and natural gas P2P exchange amount between microgrid i and other microgrid j, is the heating load demand in the region.

[0110] Microgrid operation cost: the optimization objective constraint of the whole society energy network operation is shown as follows:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] wherein, the minimum total system cost is taken as the objective, is the internal cost of microgrid i, including electricity, gas, and heat three parts of cost: , , ; , is the electricity purchase / sale price of the main power grid, is the energy storage operation coefficient, is the CHP power generation cost coefficient; is the gas purchase price of the main gas grid, is the P2G gas production cost coefficient; is the gas boiler coefficient, is the CHP heat generation cost coefficient.

[0117] S2: solving the multi-energy microgrid optimization model by using an alternating direction multiplier method to obtain point-to-point (P2P) energy exchange between microgrids;

[0118] As one of the optional embodiments, the solving the multi-energy microgrid optimization model by using an alternating direction multiplier method to obtain point-to-point (P2P) energy exchange between microgrids comprises:

[0119] Based on the multi-energy microgrid optimization model, a settlement optimization structure considering P2P energy exchange is constructed according to P2P energy flow constraints between microgrids, and an augmented Lagrangian function is constructed according to the settlement optimization structure;

[0120] The augmented Lagrangian function is solved by using an alternating direction multiplier method to obtain P2P energy exchange between microgrids.

[0121] As one of the optional embodiments, the settlement optimization structure introduces auxiliary variables in the P2P energy flow constraints;

[0122] The solving the augmented Lagrangian function by using an alternating direction multiplier method comprises:

[0123] Initializing auxiliary variables and dual variables;

[0124] Based on the auxiliary variables and the dual variables, decision variables of each microgrid and P2P energy exchange are updated according to the augmented Lagrangian function;

[0125] The auxiliary variables are updated according to the current dual variables and P2P energy exchange;

[0126] The dual variables are updated according to the current auxiliary variables and P2P energy exchange;

[0127] The original residual is calculated according to the current P2P energy exchange and auxiliary variables;

[0128] It is judged whether the original residual is less than a preset tolerance, if yes, the current P2P energy exchange is output, if not, the updating steps of the decision variables, P2P energy exchange, auxiliary variables and dual variables are repeated until the original residual is less than the preset tolerance, and the current P2P energy exchange is output.

[0129] Specifically, to synchronously follow the basic protocol and maintain the privacy and autonomy of each multi-energy microgrid, the embodiment of the application constructs a dynamic distributed double auction multi-energy settlement optimization structure based on an alternating direction multiplier method (ADMM).

[0130] where is defined as the P2P energy exchange amount between microgrid i and j to clarify the direction of power and natural gas exchange between microgrids to prevent repeated interaction, and P2P energy flow constraints are constructed. The formula of P2P energy flow constraints is as follows:

[0131]

[0132]

[0133] where, is the P2P energy exchange amount between microgrid i and j, and N is the number of microgrid structures.

[0134] Since the convergence of multiple blocks of ADMM is difficult to guarantee, auxiliary variables are introduced to optimize energy exchange decisions, and the N blocks of structure of N microgrids are equivalent to a double block structure to guarantee convergence. Specifically, auxiliary variables and dual variables are used to replace P2P energy flow constraints, and the goal is to optimize the interaction state of each microgrid while protecting privacy, that is, the optimal P2P energy exchange can be achieved by iteratively updating the interaction object microgrid j in the dynamic bilateral auction. The formula of introducing auxiliary variables is as follows:

[0135]

[0136]

[0137] where, is the auxiliary variable, is the dual variable.

[0138] On this basis, the optimization objective of microgrid i is converted into the following augmented Lagrangian form:

[0139]

[0140]

[0141] The constraints include the above-mentioned power grid interaction constraints, gas grid interaction constraints, electrical energy storage constraints, electrical-to-gas conversion device constraints, combined heat and power device constraints, gas boiler constraints, demand response constraints, energy balance constraints, and P2P energy flow constraints; wherein, is the augmented Lagrangian function, is the penalty coefficient, is the decision variable vector of microgrid i, which specifically contains the following elements as shown in the following column:

[0142]

[0143] Further, the specific steps of solving the augmented Lagrangian problem by using the ADMM algorithm are as follows:

[0144] Step 1: Update the P2P energy exchange amount :

[0145] Leveraging the decomposability of the augmented Lagrangian function, each microgrid can update its local variables in a fully distributed manner based on its own local constraints. Specifically, the solution for the local variables of microgrid i at time t is as follows:

[0146]

[0147] It is based on auxiliary variables provided by the Grid System Operator (GSO) in the τth iteration. With dual variables Update decision variables with their values Energy exchange volume with P2P After the update, Microgrid i will update the P2P energy exchange volume. It is broadcast to other micronets via GSO.

[0148] Step 2: Update auxiliary variables :

[0149] Based on dual variables Updated P2P energy exchange volume Solve and update the auxiliary variables The formula is as follows:

[0150] ,

[0151] The constraints are:

[0152] This formula can be decomposed into independent subproblems for each interaction pair i and j, where each microgrid is determined based on the P2P energy exchange received from the interaction pair. To update its auxiliary variables Specifically, by applying the KKT conditions to transform the formula, the updated formula can be derived:

[0153]

[0154] Step 3: Update the dual variable :

[0155] Each microgrid i is based on the latest P2P energy exchange volume with auxiliary variables To update the dual variable Then update the dual variable The message is sent to the GSO, which then broadcasts it to the interacting micro-network j, providing information for subsequent iterations. Based on... The symmetry of this property allows for the further derivation of the updated dual variable:

[0156]

[0157]

[0158] Available Symmetry. According to the embodiment, the multi-capacity clearing optimization structure of the dynamic distributed double auction can ensure the completion of the P2P energy exchange settlement between microgrids in the market equilibrium state.

[0159] Step 4: Convergence condition judgment:

[0160] In each iteration, according to the updated P2P energy exchange amount And the auxiliary variable The original residual is calculated:

[0161]

[0162] Wherein, The original residual.

[0163] When the original residual Is not less than the preset tolerance , repeat steps 1~4 above, until the original residual calculated Is less than the preset tolerance , the iteration calculation is terminated, and the final P2P energy exchange amount is output.

[0164] The embodiment of the application adopts the ADMM information interaction mechanism, so that each microgrid only needs to exchange limited necessary data when participating in multiple rounds of auction and subsequent price negotiation, which significantly reduces the risk of leakage of sensitive information and ensures information security.

[0165] S3: based on the P2P energy exchange amount, adopting Rubinstein bargaining solution RBS strategy to calculate P2P energy exchange price, and obtaining incentive signal of microgrid energy exchange;

[0166] As one of the optional embodiments, the P2P energy exchange price is calculated based on the P2P energy exchange amount, adopting Rubinstein bargaining solution RBS strategy to calculate P2P energy exchange price, and obtaining incentive signal of microgrid energy exchange, comprising:

[0167] Based on the RBS strategy, an affine utility function meeting the RBS negotiation condition is constructed;

[0168] The affine utility function is normalized to obtain a normalized utility function to quantify the satisfaction of the participants to the price;

[0169] Based on the normalized utility function, the P2P energy exchange price under the RBS global optimal solution condition is calculated combined with the maximum utility under the RBS strategy, to obtain the initial price;

[0170] constructing an energy sharing priority coefficient based on the P2P energy exchange amount, and calculating a final P2P energy exchange price according to a product of the initial price and the energy sharing priority coefficient.

[0171] The affine utility function includes:

[0172]

[0173]

[0174] wherein, , are the affine utility functions of the microgrids i and j respectively, , are the purchase / sale energy opportunity cost unit prices of the microgrids i and j with reference to the main grid respectively, , are the P2P energy exchange amounts of the microgrids i and j respectively, , are the proportions of the energy exchange amounts in the total demand amounts and the total surplus amounts respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of the microgrids i and j respectively.

[0175] Further, the P2P energy exchange price under the RBS global optimal solution condition is calculated based on the normalized utility function and the maximum utility under the RBS strategy, and includes:

[0176] a correlation model of the normalized utility function and the energy exchange price is constructed:

[0177]

[0178] wherein, , are the normalized utility indexes of the microgrids i and j respectively, , are the purchase / sale energy opportunity cost unit prices of the microgrids i and j with reference to the main grid respectively, , are the P2P energy exchange amounts of the microgrids i and j respectively, , are the proportions of the energy exchange amounts in the total demand amounts and the total surplus amounts respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of the microgrids i and j respectively, , The maximum allowed proportion of the P2P energy exchange amount of the microgrid i and j at time t in the total demand and the total surplus, respectively;

[0179] The maximum utility under the RBS strategy is taken as an objective function:

[0180]

[0181] Wherein, , ;

[0182] The RBS global optimal solution condition is: ;

[0183] The P2P energy exchange price under the RBS global optimal solution condition is calculated:

[0184]

[0185] Wherein, is the P2P energy exchange price of the microgrid i and j at time t derived based on the RBS strategy.

[0186] Specifically, the RBS (Rubinstein Bargaining Solution) strategy is used to establish an optimization model for the microgrids participating in the P2P energy exchange, and this process is modeled as an RBS bargaining problem with one-to-one negotiation characteristics and aiming to seek an optimal energy unit price. The profit function of the microgrids i and j in the P2P energy exchange at time t is given as follows:

[0187]

[0188]

[0189] Wherein, , are the affine utility functions of the participating microgrids i and j, and are closed convex sets on satisfying the RBS negotiation condition; , are the purchase / sale energy opportunity cost unit prices of the microgrids i and j with reference to the main grid, , are the P2P energy exchange amounts of the microgrids i and j, , are the proportions of the energy exchange amount in the total demand and the total surplus, respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of the microgrids i and j.

[0190] Further, to quantify the satisfaction of the participants' microgrids to the price, the normalized utility index model is used to normalize the affine utility function, and the formula is:

[0191]

[0192] wherein, , are the normalized utility indexes of microgrids i and j respectively, , are the maximum and minimum profits of the microgrid respectively. The normalized utility index is the part of the actual profit obtained by the participant microgrid i, j exceeding the minimum profit, accounting for the difference between the maximum profit and the minimum profit, wherein the maximum value of the normalized utility index is 1 and the minimum value is 0.

[0193] Further, the correlation model of the normalized utility function and the energy exchange price is constructed:

[0194]

[0195] wherein, , are the normalized utility indexes of microgrids i and j respectively, , are the purchase / sale energy opportunity cost unit prices of microgrids i and j referring to the main grid respectively, , are the P2P energy exchange amounts of microgrids i and j respectively, , are the proportions of the energy exchange amount in the total demand amount and the total surplus amount respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of microgrids i and j respectively, , are the maximum allowed proportions of the P2P energy exchange amount of microgrids i and j at time t in the total demand amount and the total surplus amount respectively;

[0196] The maximum utility under the RBS strategy is taken as the objective function:

[0197]

[0198] wherein, , . The objective function is a combination optimization problem, which defines the Nash bargaining product maximization problem, when , , always satisfies and , , reaches the maximum value. Therefore, when The time is the optimal condition solution. The RBS strategy forces the utility of the two parties to be equal through the target condition, which guarantees the maximization of the total utility of the two parties and ensures the satisfaction of the two parties, and ensures the fairness of the distribution.

[0199] The condition that the RBS global optimal solution needs to meet is: ;

[0200] Under the condition of the RBS global optimal solution, the P2P energy exchange price under the condition of the RBS global optimal solution is calculated:

[0201]

[0202] Among them, is the P2P energy exchange price of microgrid i and j at time t derived based on the RBS strategy.

[0203] Further, based on the obtained P2P energy exchange amount, a benefit priority distribution mechanism is constructed to determine the energy sharing priority coefficient. This mechanism aims to encourage microgrids to actively participate in energy exchange and ensure that microgrids with high contribution obtain maximum interaction benefits. The specific formula of the energy sharing priority coefficient is as follows:

[0204]

[0205] Further, combined with the P2P energy exchange price derived based on the RBS strategy and the energy sharing priority coefficient , the final P2P energy exchange price is obtained:

[0206]

[0207] The P2P energy exchange price serves as a real-time signal or settlement basis for providing the equivalent incentive or settlement value of the microgrid when executing the scheduling scheme.

[0208] Further, the embodiment of the present application integrates the P2P energy exchange amount and the RBS pricing strategy, realizes market equilibrium and ensures the fairness of bargaining. The final optimization target of each microgrid is as follows:

[0209]

[0210] Among them, the final optimization target of each microgrid combines its internal operating cost and the cost or benefit generated through P2P based on the final energy exchange , effectively improving the economic benefits of multi-energy microgrid energy scheduling.

[0211] In the embodiments of the present application, the P2P energy exchange quantity is an ideal matching result obtained in the ADMM iteration, but if there is a lack of price mechanism, there is not enough incentive for microgrids to actually perform the exchange, so the RBS bargaining strategy is introduced to obtain the optimal price, and the P2P energy exchange price is an incentive signal for driving the microgrids to perform scheduling instructions according to the optimization result, thereby realizing resource coordination optimization and improving system stability.

[0212] S4: obtaining a comprehensive scheduling scheme of the microgrids according to the P2P energy exchange quantity and the incentive signal.

[0213] Specifically, the scheduling scheme includes the energy quantity stored, converted and used by each microgrid in each period, and the corresponding energy pricing, and the energy exchange quantity and the energy exchange price are coupled in the same scheduling scheme, which is then issued to each microgrid in an integrated manner, and each microgrid can be directly used for local execution and subsequent settlement, without the need for additional price matching or communication, thereby reducing communication overhead and improving efficiency.

[0214] In some embodiments, the incentive signal is also used for calculation and update of the P2P energy exchange quantity, so that the energy scheduling is more adapted to the demand of the microgrids, thereby enhancing the synergy and robustness of the entire system and improving the resource utilization rate. For example, the incentive signal can be used as a step adjustment factor or coupling weight of a local optimization subproblem to accelerate the iteration convergence and enhance the system robustness.

[0215] The effects of the present application will be described below in conjunction with specific examples:

[0216] Figure 2 The overall structure of the system proposed by the present application is depicted, and the core is three energy microgrids (MG1, MG2, MG3), each of which is connected to the main power grid and the main gas grid. In the operation mode setting of the system, a bidirectional energy interaction relationship is established between the power microgrid and the main power grid connected thereto. However, for natural gas supply, the microgrid adopts a unidirectional mode, i.e., it can only purchase gas source from the main gas grid. Such arrangement is on the one hand to take advantage of the feasibility of reverse transmission of electric energy and the maturity of the existing electric power market, and on the other hand to avoid the inherent complex operation requirements and safety management difficulties of natural gas system interaction (especially reverse flow). Figure 3 The specific data of the power, natural gas and heat loads corresponding to each microgrid in the present application are listed in detail, and each microgrid has the ability to handle these three loads at the same time.

[0217] Figure 4For each micro-grid electricity, gas P2P energy exchange diagram of the application, it can be seen that the micro-grid reduces cost and increases flexibility through effective electricity and gas demand response, optimizes power use according to time-of-use electricity price (peak reduction / valley increase) and uses gas power generation strategy (considering price difference and CHP / gas boiler priority gas use), and finally makes the natural gas terminal load continue to decrease. The P2P energy scheduling method proposed in the application significantly improves the operation flexibility of multi-energy carriers and the comprehensive utilization efficiency of the whole system by promoting the bidirectional flow of energy and mutual assistance and sharing among participants, and effectively combining resource complementation and peak clipping and valley filling strategies.

[0218] Figure 5 For each micro-grid electricity, gas purchase and sale interaction state diagram of the application, it can be seen that the proposed P2P energy scheduling method promotes the micro-grid to preferentially internal mutual aid, and only when the time-varying electricity, gas and heat comprehensive demand or surplus cannot be met through P2P, energy interaction with the main grid is performed, thereby effectively reducing the dependence on the main grid. Figure 6 For each micro-grid typical time electricity, gas P2P energy flow diagram provided by the embodiment of the application, it can be seen that the micro-grid preferentially selects internal electricity and gas mutual aid (including the use of P2G converted gas) rather than interaction with the main grid in a specific period of time, so as to improve resource utilization and maximize individual and overall benefits.

[0219] Figure 7 For each micro-grid cost iteration diagram of the application, it is shown that the ADMM algorithm successfully and accurately optimizes the P2P energy scheduling among multi-energy micro-grids while protecting privacy through an effective iterative convergence process, and finally reaches an optimal solution.

[0220] The application uses the ADMM algorithm to coordinate energy mutual aid and optimal scheduling among multi-energy micro-grids while protecting privacy, and reduces system operation cost and dependence on the main grid through a fair pricing mechanism, thereby improving participant satisfaction and resource utilization.

[0221] Correspondingly, the application also provides a multi-energy micro-grid point-to-point energy scheduling device, which can realize all processes of the multi-energy micro-grid point-to-point energy scheduling method in the above embodiment.

[0222] Please refer to Figure 8 , Figure 8 is a structural schematic diagram of a multi-energy micro-grid point-to-point energy scheduling device provided by the embodiment of the application. The multi-energy micro-grid point-to-point energy scheduling device comprises:

[0223] The micro-grid model construction module 801 is configured to construct a multi-energy micro-grid optimization model comprising electricity, gas and heat energy according to network coupling constraints, device characteristic constraints and scheduling operation constraints of the energy micro-grid.

[0224] The energy exchange calculation module 802 is configured to perform distributed iterative solving on the multi-energy micro-grid optimization model by using an alternating direction multiplier algorithm to obtain point-to-point (P2P) energy exchange between micro-grids;

[0225] The energy exchange pricing module 803 is configured to calculate a P2P energy exchange price by using a Rubinstein bargaining solution (RBS) strategy based on the P2P energy exchange to obtain an incentive signal for micro-grid energy exchange;

[0226] The micro-grid energy dispatching module 804 is configured to obtain a comprehensive dispatching scheme of the micro-grid according to the P2P energy exchange and the incentive signal.

[0227] Preferably, the network coupling constraints include power grid interaction constraints and gas grid interaction constraints.

[0228] The device characteristic constraints include electric energy storage constraints, electric-to-gas device constraints, combined heat and power device constraints, and gas boiler constraints.

[0229] The dispatching operation constraints include demand response constraints, energy balance constraints, and micro-grid operation cost constraints.

[0230] Preferably, the distributed iterative solving on the multi-energy micro-grid optimization model by using the alternating direction multiplier algorithm to obtain the P2P energy exchange between micro-grids includes:

[0231] Based on the multi-energy micro-grid optimization model, a clearing optimization structure considering P2P energy exchange is constructed according to P2P energy flow constraints between micro-grids, and an augmented Lagrangian function is constructed according to the clearing optimization structure.

[0232] The augmented Lagrangian function is iteratively solved by using the alternating direction multiplier algorithm to obtain the P2P energy exchange between micro-grids.

[0233] Preferably, the clearing optimization structure introduces auxiliary variables in the P2P energy flow constraints.

[0234] The iterative solving of the augmented Lagrangian function by using the alternating direction multiplier algorithm includes:

[0235] The auxiliary variables and the dual variables are initialized.

[0236] Based on the auxiliary variables and the dual variables, the decision variables of each micro-grid and the P2P energy exchange are updated according to the augmented Lagrangian function.

[0237] The auxiliary variables are updated according to the current dual variables and the P2P energy exchange.

[0238] The dual variables are updated according to the current auxiliary variables and the P2P energy exchange.

[0239] calculating an original residual according to the current P2P energy exchange amount and the auxiliary variable;

[0240] judging whether the original residual is less than a preset tolerance, if yes, outputting the current P2P energy exchange amount, if not, repeating the updating steps of the decision variable, the P2P energy exchange amount, the auxiliary variable and the dual variable until the original residual is less than the preset tolerance, and outputting the current P2P energy exchange amount.

[0241] Preferably, based on the P2P energy exchange amount, a Rubinstein bargaining solution RBS strategy is used to calculate a P2P energy exchange price, to obtain an incentive signal of micro-grid energy exchange, comprising:

[0242] constructing an affine utility function meeting RBS negotiation conditions based on the RBS strategy;

[0243] normalizing the affine utility function to obtain a normalized utility function, to quantify the satisfaction of participants to the price;

[0244] based on the normalized utility function, combining the maximum utility under the RBS strategy to calculate the P2P energy exchange price under the RBS global optimal solution condition, to obtain an initial price;

[0245] constructing an energy sharing priority coefficient based on the P2P energy exchange amount, and calculating a final P2P energy exchange price according to the product of the initial price and the energy sharing priority coefficient.

[0246] Preferably, the affine utility function comprises:

[0247]

[0248]

[0249] wherein, , are affine utility functions of micro-grid i and j respectively, , are purchase / sale opportunity cost unit prices of micro-grid i and j referring to the main grid respectively, , are P2P energy exchange amounts of micro-grid i and j respectively, , are proportions of the energy exchange amount in the total demand and the total surplus respectively, is a P2P energy unit price, , are energy transmission cost coefficients of micro-grid i and j respectively.

[0250] Preferably, the P2P energy exchange price under the RBS global optimal solution condition is calculated based on the normalized utility function combined with the maximized utility under the RBS strategy, comprising:

[0251] A correlation model of the normalized utility function and the energy exchange price is constructed:

[0252]

[0253] wherein, , are normalized utility indexes of the microgrids i and j respectively, , are purchase / sale energy opportunity cost unit prices of the microgrids i and j referring to the main grid respectively, , are P2P energy exchange amounts of the microgrids i and j respectively, , are proportions of the energy exchange amounts in total demand amounts and total surplus amounts respectively, is a P2P energy unit price, , are energy transmission cost coefficients of the microgrids i and j respectively, , are maximum allowed proportions of the P2P energy exchange amounts of the microgrids i and j in total demand amounts and total surplus amounts respectively at the time t;

[0254] The maximized utility under the RBS strategy is taken as an objective function:

[0255]

[0256] wherein, ,

[0257] The RBS global optimal solution condition is:

[0258] The P2P energy exchange price under the RBS global optimal solution condition is calculated:

[0259]

[0260] wherein, is the P2P energy exchange price of the microgrids i and j at the time t derived based on the RBS strategy.

[0261] In the specific implementation, the working principle, control flow and technical effects of the multi-energy microgrid point-to-point energy dispatching device provided by the embodiments of the present application are the same as those of the multi-energy microgrid point-to-point energy dispatching method in the above embodiments, and will not be repeated here.

[0262] Referring to Figure 9 ​​, Figure 9 is a structural block diagram of a computer device provided by an embodiment of the present application, and the computer device includes a processor 901, a memory 902, and a computer program stored in the memory 902 and executable on the processor 901. The processor 901 implements the steps in the multi-energy micro-grid point-to-point energy scheduling method embodiment described above when executing the computer program. Alternatively, the processor 901 implements the functions of each module / unit in each device embodiment described above when executing the computer program.

[0263] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 902 and executed by the processor 901 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0264] The computer device can include, but is not limited to, the processor 901 and the memory 902. Those skilled in the art can understand that the schematic diagram is only an example of the computer device and does not limit the computer device, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, etc.

[0265] The processor 901 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 901 is the control center of the computer device, which connects each part of the computer device through various interfaces and lines.

[0266] The memory 902 can be used to store the computer programs and / or modules, and the processor 901 realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 902, and calling the data stored in the memory 902. The memory 902 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory 902 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0267] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by the processor 901. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0268] The embodiment of the present application also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the multi-energy micro-grid point-to-point energy scheduling method provided in any one of the above-mentioned embodiments.

[0269] The embodiment of the present application provides a kind of multi-energy microgrid point-to-point energy scheduling method, device, equipment and medium, its beneficial effect is in at:By the network coupling constraint of energy microgrid, equipment characteristic constraint and scheduling operation constraint, the multi-energy microgrid optimization model containing electricity, gas, heat energy is constructed, can carry out multi-energy coordination, it is favorable to resource scheduling;Adopt alternate direction multiplier algorithm to the multi-energy microgrid optimization model is distributed iteration solution, obtain the point-to-point P2P energy exchange amount between each microgrid, only need to exchange limited necessary data, significantly reduce the risk of sensitive information leakage, also guarantee the feasibility and convergence of energy distribution;Based on the P2P energy exchange amount, RBS strategy is used to calculate P2P energy exchange price using Rubinstein bargaining solution, obtain the incentive signal of microgrid energy exchange, improve the fairness of each microgrid energy interaction, it is favorable to enhance the cooperativity and robustness of whole system, improve resource utilization rate;According to the P2P energy exchange amount and the incentive signal, obtain the comprehensive scheduling scheme of microgrid, solve the problem that multi-energy microgrid P2P energy interaction lacks multi-energy coordination, fair pricing and privacy protection in the prior art.

[0270] The above is the preferred embodiment of the present application, it should be pointed out, for ordinary skilled person in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements also regarded as the protection scope of the present application.

Claims

1. A multi-energy microgrid point-to-point energy dispatching method, characterized in that, The method comprises the steps of: According to the network coupling constraint, equipment characteristic constraint and scheduling operation constraint of the energy microgrid, a multi-energy microgrid optimization model containing electric, gas and heat energy is constructed; The multi-energy microgrid optimization model is solved by using an alternating direction multiplier algorithm to obtain point-to-point (P2P) energy exchange between microgrids; Based on the P2P energy exchange, a Rubinstein bargaining solution (RBS) strategy is used to calculate a P2P energy exchange price as an incentive signal for microgrid energy exchange; According to the P2P energy exchange and the incentive signal, a comprehensive scheduling scheme of the microgrid is obtained; The method comprises the steps of: Based on the RBS strategy, an affine utility function satisfying the RBS negotiation condition is constructed; The affine utility function is normalized to obtain a normalized utility function to quantify the satisfaction of participants to the price; Based on the normalized utility function, the P2P energy exchange price under the RBS global optimal solution condition is calculated by combining the maximum utility under the RBS strategy to obtain an initial price; Based on the P2P energy exchange, an energy sharing priority coefficient is constructed, and the final P2P energy exchange price is calculated based on the product of the initial price and the energy sharing priority coefficient.

2. The multi-energy microgrid point-to-point energy dispatching method of claim 1, wherein, The network coupling constraint comprises a power grid interaction constraint and a gas grid interaction constraint; The equipment characteristic constraint comprises an electric energy storage constraint, an electric-to-gas equipment constraint, a combined heat and power equipment constraint and a gas boiler constraint; The scheduling operation constraint comprises a demand response constraint, an energy balance constraint and a microgrid operation cost constraint.

3. The multi-energy microgrid point-to-point energy dispatching method of claim 1, wherein, The method comprises the steps of: Based on the multi-energy microgrid optimization model, a clearing optimization structure considering P2P energy exchange is constructed according to the P2P energy flow constraint between microgrids, and an augmented Lagrangian function is constructed according to the clearing optimization structure; The augmented Lagrangian function is solved by using an alternating direction multiplier algorithm to obtain the P2P energy exchange between microgrids.

4. The multi-energy microgrid point-to-point energy dispatching method of claim 3, wherein, The clearing optimization structure introduces an auxiliary variable in the P2P energy flow constraint; The method comprises the steps of: The auxiliary variable and the dual variable are initialized; Based on the auxiliary variable and the dual variable, the decision variable and the P2P energy exchange of each microgrid are updated according to the augmented Lagrangian function; The auxiliary variable is updated according to the current dual variable and the P2P energy exchange; The dual variable is updated according to the current auxiliary variable and the P2P energy exchange; The original residual error is calculated according to the current P2P energy exchange and the auxiliary variable; It is judged whether the original residual error is less than a preset tolerance, if yes, the current P2P energy exchange is output, if not, the updating steps of the decision variable, the P2P energy exchange, the auxiliary variable and the dual variable are repeated until the original residual error is less than the preset tolerance, and the current P2P energy exchange is output.

5. The multi-energy microgrid point-to-point energy dispatching method of claim 1, wherein, The affine utility function comprises: wherein, , are the affine utility functions of the microgrids i and j, respectively, , are the purchase / sell energy opportunity cost unit prices of the microgrids i and j with respect to the main grid, respectively, , are the P2P energy exchange quantities of the microgrids i and j, respectively, , are the proportions of the energy exchange quantities to the total demand quantities and the total surplus quantities of the microgrids i and j, respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of the microgrids i and j, respectively.

6. The multi-energy microgrid point-to-point energy dispatching method of claim 5, wherein, The P2P energy exchange price under the RBS global optimal solution condition is calculated based on the normalized utility function combined with the maximum utility under the RBS strategy, including: An associated model of the normalized utility function and the energy exchange price is constructed: wherein, , are the normalized utility indices of microgrids i and j, respectively, , are the buying / selling energy opportunity cost unit prices of microgrids i and j with respect to the main grid, respectively, , are the P2P energy exchange amounts of microgrids i and j, respectively, , are the proportions of the energy exchange amounts in the total demand amounts and the total surplus amounts of microgrids i and j, respectively, is the P2P energy unit price, , are the energy transmission cost coefficients of microgrids i and j, respectively, , are the maximum allowed proportions of the P2P energy exchange amounts of microgrids i and j at time t in the total demand amounts and the total surplus amounts of microgrids i and j, respectively; The maximum utility under the RBS strategy is taken as an objective function: wherein , ; The RBS global optimal solution condition is: ; The P2P energy exchange price under the RBS global optimal solution condition is calculated: wherein, P2P energy exchange price of microgrid i and j at time t derived based on RBS strategy.

7. A multi-energy microgrid point-to-point energy dispatching device, characterized in that, including: A microgrid model construction module is configured to construct a multi-energy microgrid optimization model containing electric, gas and heat energy according to network coupling constraints, device characteristic constraints and dispatching operation constraints of the energy microgrid; An energy exchange calculation module is configured to obtain point-to-point P2P energy exchange amount between microgrids by using an alternating direction multiplier algorithm to distribute and iteratively solve the multi-energy microgrid optimization model; An energy exchange pricing module is configured to calculate P2P energy exchange price as an incentive signal for microgrid energy exchange by using a Rubinstein bargaining solution RBS strategy based on the P2P energy exchange amount; A microgrid energy dispatching module is configured to obtain a comprehensive dispatching scheme of the microgrid according to the P2P energy exchange amount and the incentive signal; The P2P energy exchange price is calculated based on the P2P energy exchange amount by using a Rubinstein bargaining solution RBS strategy, including: An affine utility function meeting RBS negotiation conditions is constructed based on the RBS strategy; The affine utility function is normalized to obtain a normalized utility function to quantify the satisfaction of participants to the price; The P2P energy exchange price under the RBS global optimal solution condition is calculated based on the normalized utility function combined with the maximum utility under the RBS strategy to obtain an initial price; An energy sharing priority coefficient is constructed based on the P2P energy exchange amount, and the final P2P energy exchange price is calculated based on the product of the initial price and the energy sharing priority coefficient.

8. A computer device, comprising: The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor, and the processor implements the multi-energy microgrid point-to-point energy dispatching method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor, and the processor implements the multi-energy microgrid point-to-point energy dispatching method according to any one of claims 1 to 6 when executing the computer program.

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