Isolated multi-microgrid multi-energy sharing scheduling method, system, terminal and storage medium

Abstract

The present invention discloses a multi-energy sharing scheduling method, system, terminal and storage medium for isolated multi-microgrids, which incorporates carbon trading and renewable energy consumption into the scheduling optimization of the system, first optimizes the electric load, and then performs double-layer sharing scheduling of electric energy and thermal energy, which can effectively improve the survivability of isolated microgrids. The multi-energy sharing scheduling system includes two stages. In the first stage, the electric load optimization on the demand side is used to maximize the consumption of renewable energy. In the second stage, carbon trading and energy sharing mechanisms are introduced to perform multi-energy sharing and mutual assistance scheduling of multi-microgrids, and a distributed algorithm based on the alternating direction multiplier method is developed to achieve scheduling. The present invention provides a low-carbon economic operation method for the operation of a multi-microgrid system. The two-stage scheduling strategy allows electric energy and thermal energy to be scheduled and utilized between multiple microgrids, improves the overall energy utilization efficiency of the system, and effectively reduces operating costs and carbon emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid operation optimization, and in particular to an isolated island multi-microgrid multi-energy sharing scheduling method, system, terminal and storage medium. Background Art

[0002] In recent years, in order to reduce the use of traditional fossil fuels, large-scale renewable energy has been connected to microgrids to power them. However, with the access of distributed energy, the problem of energy consumption has become a key concern. At the same time, most of the isolated microgrids are located in remote areas, mountain villages or islands. They are disconnected from the main power grid and have weak anti-interference and survival capabilities. Therefore, it is crucial for the microgrid system to achieve efficient and economic operation by interconnecting multiple isolated microgrids to form an interconnected microgrid system and conduct multi-energy mutual assistance and carbon trading. How to achieve low-carbon economic operation of interconnected microgrids while ensuring maximum consumption of renewable energy and minimizing carbon emissions has become a key issue in energy sharing in the microgrid field.

[0003] In the existing system, the factors considered in the optimal scheduling of some interconnected multi-microgrids are different from the specific scheduling strategies. Some systems only consider economic factors, not environmental factors. Later, some scholars considered environmental factors when studying multi-microgrid systems. However, when considering carbon emissions, they only take carbon emissions as system constraints, and do not consider the role of the carbon trading market in the optimal scheduling of multi-microgrid systems. As the research becomes more and more in-depth, research on energy sharing has received widespread attention. In the existing energy sharing research, some scholars have studied the sharing of electric energy between multi-microgrid entities. However, there is still a gap in the current research on multi-energy sharing. When realizing the optimal scheduling of multi-microgrid systems, relevant technologies will be used to achieve it. In the existing technologies, there are centralized and decentralized methods for optimizing the scheduling of multi-microgrids. However, centralized scheduling requires the construction of mathematical models and the acquisition of all parameters, which may damage the security and privacy of the system; decentralized methods, due to the lack of coordination, it is difficult to achieve energy sharing.

[0004] There are some existing schemes, such as: the publication number is CN115619053A, the publication date is January 17, 2023, and the name of the invention is "A method for optimizing the quality of carbon emissions in multi-microgrid area collaboration". This scheme studies the method for optimizing the quality of carbon emissions in multi-microgrid area collaboration based on the carbon emissions of the energy system, including two stages, day-ahead and day-intraday, to achieve collaboration between multiple microgrids, but it does not consider how to reduce carbon emissions and the impact of carbon trading. The publication number is CN112564102A, the publication date is March 26, 2021, and the name of the invention is "Multi-microgrid load optimization scheduling method and system". This scheme optimizes and schedules multiple microgrids in real time based on a non-cooperative game strategy. Each microgrid participates in the process of power interaction with the large power grid, but the power interaction between multiple microgrid entities is not considered. The publication number is CN108229025A, the publication date is June 29, 2018, and the name of the invention is "An economic optimization scheduling method for a combined heat, power and multi-microgrid active distribution system". This scheme analyzes the equipment models in the combined heat, power and multi-microgrid and establishes an economic optimization scheduling model; according to the power interaction value of the interconnection line between the combined heat, power and multi-microgrid and the active distribution network, the microgrid body is optimized and scheduled, and finally solved through Complex. The solver it uses is a centralized solution, which is still insufficient in privacy protection.

[0005] In summary, a distributed method with a more flexible energy management mode and the ability to effectively protect the privacy of multi-microgrid systems is needed, incorporating multi-energy sharing combined with a carbon trading mechanism to efficiently achieve low-carbon economic dispatch of multi-microgrids. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an isolated multi-microgrid multi-energy shared scheduling method, system, terminal and storage medium, which can reduce carbon emissions and the economic cost of system operation when performing multi-energy mutual aid shared scheduling, improve the utilization efficiency of renewable energy and the coordination of multiple energy sources, and improve the stability and economy of the multi-microgrid scheduling system.

[0007] In a first aspect, a method for multi-energy sharing scheduling of isolated multi-microgrids is provided, comprising the following steps:

[0008] S1: Modeling an interconnected multi-microgrid system, which contains multiple forms of energy that can flow between multiple isolated microgrids;

[0009] S2: Carry out the first phase of system load optimization, establish a load transfer mechanism, construct a load optimization problem, and obtain the optimal load demand curve;

[0010] S3: According to the optimal electric load demand curve, the carbon trading mechanism and multi-energy sharing mechanism are introduced in the energy scheduling of the second stage, and a distributed algorithm is used to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each device to obtain the scheduling strategy;

[0011] S4: Real-time scheduling of interconnected multi-microgrid systems based on scheduling strategies.

[0012] Furthermore, each isolated microgrid in the interconnected multi-microgrid system includes a combined heat and power (CHP), a gas boiler (GB), an energy storage system (ES), and renewable energy (RES); each isolated microgrid in the interconnected multi-microgrid system is connected to a natural gas plant and contains three types of energy: electricity, natural gas, and thermal energy; the isolated microgrid includes two types of loads: electrical load and thermal load.

[0013] Furthermore, the modeling of the interconnected multi-microgrid system includes constructing each isolated island microgrid model and energy supply balance constraints;

[0014] Among them, each isolated microgrid model includes a combined heat and power CHP model, a gas boiler GB model, an energy storage system ES model, and a renewable energy RES model;

[0015] The combined heat and power (CHP) model is expressed as follows:

[0016]

[0017]

[0018] In the formula, , , They represent the power generation power, thermal power and gas consumption of the cogeneration unit of the i-th island microgrid in period t respectively; , They represent the electricity generation efficiency and heat generation efficiency of the cogeneration of the i-th isolated microgrid, respectively; T is the set of scheduling periods, and N is the set of isolated microgrids in the interconnected multi-microgrid system;

[0019] The GB model of gas boiler is expressed as follows:

[0020]

[0021] In the formula, represents the combustion efficiency of the gas boiler in the i-th island microgrid, and They represent the heating power and natural gas consumption of the gas boiler of the i-th island microgrid during period t;

[0022] The ES model of the energy storage system is expressed as follows:

[0023]

[0024] In the formula, and They represent the energy storage status of the energy storage system of the i-th island microgrid in period t and period t-1 respectively; and They represent the charging power and discharging power of the energy storage system of the i-th island microgrid during period t; and They represent the charging efficiency and discharging efficiency of the energy storage system of the i-th island microgrid respectively; ;

[0025] The renewable energy RES model is expressed as follows:

[0026]

[0027]

[0028]

[0029] In the formula, and They represent the photovoltaic power generation and wind power generation of the i-th island microgrid in period t respectively; and They represent the maximum power of photovoltaic power generation and wind power generation of the i-th island microgrid respectively; represents the total renewable energy generation power of the i-th island microgrid in period t;

[0030] The energy balance constraint is expressed as follows:

[0031]

[0032]

[0033] In the formula, and They represent the electric load and thermal load of the i-th island microgrid in period t respectively.

[0034] Furthermore, step S2 specifically includes:

[0035] Obtain the predicted photovoltaic power generation curve and wind power generation curve of each isolated island microgrid, as well as the initial electric load demand curve of each isolated island microgrid;

[0036] The electric load transfer model is established and expressed as follows:

[0037]

[0038]

[0039] In the formula, They represent the initial electric load of the i-th island microgrid in period t and the actual electric load after the first stage of electric load optimization; represents the actual surplus renewable energy power of the i-th island microgrid in period t, represents the total renewable energy generation power of the i-th island microgrid in period t; and They represent the electric power transferred out or in from the i-th island microgrid in period t respectively; and They represent the load transfer coefficients of the i-th island microgrid transferred out or in during period t;

[0040] Establish the first-stage objective function of minimizing the residual cost of renewable energy and the compensation cost of user electricity dissatisfaction , and expressed as follows:

[0041]

[0042] In the formula, represents the first stage electrical load optimization cost; represents the residual cost of renewable energy in period t, It represents the compensation cost of user electricity dissatisfaction during period t; and They represent the unit cost of remaining renewable energy and the unit compensation cost of user electricity dissatisfaction respectively; T is the dispatch period;

[0043] The optimal electric load demand curve is obtained based on the electric load transfer model and the objective function solution.

[0044] Furthermore, in step S3, the carbon trading mechanism is expressed by the following formula:

[0045]

[0046] In the formula, Represents the price of carbon trading in the carbon trading market; represents the carbon trading cost, and They represent the free carbon quotas that can be obtained per unit power of natural gas heat-based equipment and the free carbon quotas that can be obtained per unit power of renewable energy. Indicates the carbon dioxide emission coefficient of natural gas combustion; , They represent the thermal power and natural gas consumption of the cogeneration unit of the i-th island microgrid in period t, and They represent the heat generation power and natural gas consumption of the gas boiler of the i-th island microgrid in period t, respectively; T is the scheduling period;

[0047] Establish the second-stage objective function to minimize the economic cost of the interconnected multi-microgrid system , and expressed as follows:

[0048]

[0049] In the formula, represents the energy dispatch cost of the second stage; represents the system natural gas consumption cost, represents the eventual cost reductions of renewable energy; represents the first-stage electric load optimization cost; N is the number of isolated microgrids in the interconnected multi-microgrid system;

[0050] A distributed energy optimization algorithm based on alternating direction multiplication is used to solve the exchange of electric and thermal energy between multiple isolated island microgrids and decide the power generation power of each device to obtain the scheduling strategy.

[0051] Furthermore, the distributed energy optimization algorithm based on alternating direction multiplication is used to solve the exchange amount of electric energy and thermal energy between multiple isolated island microgrids and decide the power generation power of each device, specifically including:

[0052] S31: Construct the optimized augmented Lagrangian function corresponding to the second-stage objective function of the interconnected multi-microgrid system; introduce the Lagrangian multiplier and , and consider that the power sharing between multiple isolated microgrids must meet , thermal power sharing needs to meet , the optimized augmented Lagrangian function L corresponding to the second-stage objective function of the interconnected multi-microgrid system is expressed as follows:

[0053]

[0054] In the formula, represents the economic cost of the i-th island microgrid; and is the penalty parameter, is the power sharing value between the ith island microgrid and the jth island microgrid in period t, is the power sharing value of the j-th island microgrid and the i-th island microgrid in period t; is the thermal power sharing value between the i-th island microgrid and the j-th island microgrid in period t, is the thermal power sharing value of the jth island microgrid and the i-th island microgrid in period t; N\i represents the set of island microgrids excluding the i-th island microgrid in the interconnected multi-microgrid system;

[0055] S32: Each island microgrid IMG updates its own energy sharing strategy through local calculations between IMGs, and IMGs only exchange information about energy sharing. The specific update strategies for the electric power sharing value and the thermal power sharing value are as follows:

[0056]

[0057] In the formula, is the power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration, The thermal power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration; , Lagrangian calculation functions representing the electric power sharing value and the thermal power sharing value respectively;

[0058] According to the results of the electric power sharing value and thermal power sharing value of the i-th island microgrid, the electric power sharing value and thermal power sharing value of the j-th island microgrid at the k+1th iteration are updated. The specific update strategy is as follows:

[0059]

[0060] In the formula, is the power sharing value of the jth island microgrid and the i-th island microgrid in period t at the k+1th iteration, The thermal energy sharing value between the jth island microgrid and the ith island microgrid in period t at the k+1th iteration;

[0061] S33: Update the Lagrange multiplier; according to the updated values ​​of the electric energy and thermal energy sharing values, the Lagrange multiplier is updated in sequence, and its specific mathematical expression is as follows:

[0062]

[0063]

[0064] In the formula, , represents the Lagrange multiplier at the k+1th iteration;

[0065] S34: Repeat steps S32 to S33 until the iteration is terminated to obtain the optimal decision variable value corresponding to the objective function.

[0066] Furthermore, the renewable energy in each isolated microgrid in the interconnected multi-microgrid system includes at least one of wind energy and solar energy.

[0067] In the second aspect, an isolated multi-microgrid multi-energy sharing dispatching system is provided, comprising:

[0068] An interconnected multi-microgrid system including multiple isolated microgrids, wherein the interconnected multi-microgrid system includes multiple forms of energy, and the multiple forms of energy can flow between multiple isolated microgrids;

[0069] The energy manager of each isolated microgrid has an isolated microgrid model and energy balance constraints.

[0070] The energy manager of each island microgrid is configured to perform the first phase of the system’s electrical load optimization, establish an electrical load transfer mechanism, construct an electrical load optimization problem, and obtain the optimal electrical load demand curve;

[0071] The coordinator interacts with each isolated microgrid and is configured to execute according to the optimal electric load demand curve, introduce carbon trading mechanism and multi-energy sharing mechanism in the second stage of energy scheduling, and use distributed algorithm to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each equipment, obtain the scheduling strategy, and perform real-time scheduling of the interconnected multi-microgrid system based on the scheduling strategy.

[0072] In a third aspect, an electronic terminal is provided, comprising:

[0073] Memory on which computer programs or instructions are stored;

[0074] The processor is used to load and execute the computer program or instructions to implement the isolated island multi-microgrid multi-energy sharing scheduling method as described above.

[0075] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the above-mentioned isolated island multi-microgrid multi-energy sharing scheduling method is implemented.

[0076] The present invention proposes a multi-energy sharing scheduling method, system, terminal and storage medium for isolated multi-microgrids, which has the following beneficial effects: Since the present invention includes a two-stage scheduling strategy, it can overcome the problem that a large number of traditional renewable energy sources are connected to microgrids, resulting in the inability to maximize absorption and waste. At the same time, the mutual sharing of electric energy and thermal energy between multiple microgrids is included, which improves the efficient utilization and coordination of energy and reduces the social operation cost of the system. The energy equipment model for designing the interconnected multi-microgrid system and the model for the first-stage electric load optimization scheduling are constructed to facilitate the design of efficient energy management scheduling strategies. The carbon trading model is introduced. When the system has a surplus of carbon quotas, it is sold to the market for profit. When the system is short of carbon quotas, carbon quotas are purchased from the market. Finally, a scheduling strategy for the joint optimization of carbon trading and multi-energy sharing is formed, and a distributed solution model based on the alternating direction multiplier method is designed to solve the target optimization problem of the system, obtain the optimal decision variable value, realize efficient and economic operation scheduling, improve the operation stability of the multi-microgrid system, and achieve the social operation goal of economic and low-carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0078] Figure 1 This is a flow chart of a multi-energy sharing scheduling method for multi-microgrids on isolated islands provided by an embodiment of the present invention;

[0079] Figure 2 It is a structural diagram of an isolated island multi-microgrid multi-energy sharing scheduling system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0080] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0081] In the existing research on energy sharing, some scholars have studied the sharing of electricity between multi-microgrid entities, but there is still a gap in the research on multi-energy sharing of interconnected multi-microgrids. When realizing the optimal scheduling of multi-microgrid systems, the existing technologies have centralized and decentralized methods for optimizing the scheduling of multi-microgrids. However, centralized scheduling requires the construction of mathematical models and the acquisition of all parameters, which may damage the security and privacy of the system; decentralized methods, due to the lack of coordination, are difficult to achieve energy sharing.

[0082] Based on this, the present invention provides a method, system, terminal and storage medium for multi-energy sharing scheduling of isolated multi-microgrids, constructs the optimization problem of two-stage low-carbon economic scheduling of interconnected multi-microgrid systems, maximizes the consumption of renewable energy in the first stage, and optimizes the scheduling of multi-energy sharing and carbon trading in the second stage, obtains the optimal low-carbon economic scheduling scheme and implements it in the multi-microgrid system. The present invention can reduce carbon emissions and the economic cost of system operation when performing multi-energy mutual aid and sharing scheduling, improve the utilization efficiency of renewable energy and the coordination of multiple energy sources, and improve the stability and economy of the scheduling of interconnected multi-microgrid systems. The scheme of the present invention is described in detail below in conjunction with specific embodiments.

[0083] The embodiment of the present invention provides an isolated island multi-microgrid multi-energy sharing scheduling method, comprising the following steps:

[0084] S1: Modeling the interconnected multi-microgrid system, which contains various forms of energy that can flow between multiple isolated microgrids.

[0085] In this embodiment, each isolated microgrid in the interconnected multi-microgrid system includes a combined heat and power (CHP), a gas boiler (GB), an energy storage system (ES), and a renewable energy (RES). It should be noted that the interconnected multi-microgrid system can be heterogeneous, and heterogeneity reflects the structure of renewable energy. Some isolated microgrids contain wind energy and solar energy, while some only contain wind energy or solar energy. There are also differences in the settings of equipment parameters. Each isolated microgrid IMG in the interconnected multi-microgrid system is connected to a natural gas plant and contains three types of energy: electricity, natural gas, and thermal energy; the isolated microgrid IMG includes two types of loads: electrical load and thermal load. In the energy management of the multi-microgrid interconnected system, the scheduling range within a day is defined as the scheduling period set T={1, …, T}, where T is the scheduling period. For example, in this embodiment, T=24, then one hour is a scheduling time scale.

[0086] Specifically, modeling the interconnected multi-microgrid system includes constructing each isolated microgrid model and energy balance constraints; wherein each isolated microgrid model includes a combined heat and power CHP model, a gas boiler GB model, an energy storage system ES model, and a renewable energy RES model.

[0087] More specifically, the models and energy balance constraints are as follows:

[0088] The combined heat and power (CHP) model is expressed as follows:

[0089]

[0090]

[0091] In the formula, , , They represent the power generation power, thermal power and gas consumption of the cogeneration unit of the i-th island microgrid in period t respectively; , They represent the power generation efficiency and heat generation efficiency of the cogeneration of the i-th island microgrid respectively; T is the scheduling period set, and N is the set of island microgrids in the interconnected multi-microgrid system.

[0092] The GB model of gas boiler is expressed as follows:

[0093]

[0094] In the formula, represents the combustion efficiency of the gas boiler in the i-th island microgrid, and They represent the heating power and natural gas consumption of the gas boiler of the i-th island microgrid in period t respectively.

[0095] The ES model of the energy storage system is expressed as follows:

[0096]

[0097] In the formula, and They represent the energy storage status of the energy storage system of the i-th island microgrid in period t and period t-1 respectively; and They represent the charging power and discharging power of the energy storage system of the i-th island microgrid in period t respectively; and They represent the charging efficiency and discharging efficiency of the energy storage system of the i-th island microgrid respectively; Indicates the duration of each scheduling period, which is 1 hour in this embodiment.

[0098] The renewable energy RES model is expressed as follows:

[0099]

[0100]

[0101]

[0102] In the formula, and They represent the photovoltaic power generation and wind power generation of the i-th island microgrid in period t respectively; and They represent the maximum power of photovoltaic power generation and wind power generation of the i-th island microgrid respectively; It represents the total renewable energy power generation of the i-th island microgrid in period t.

[0103] The energy balance constraint is expressed as follows:

[0104]

[0105]

[0106] In the formula, and They represent the electric load and thermal load of the i-th island microgrid in period t respectively.

[0107] For this embodiment, after the energy equipment modeling in the above steps is completed, consider that N isolated island microgrids are connected, and the entire interconnected multi-microgrid system structure is as follows: Figure 2 As shown in Figure 2, each isolated microgrid IMG has its own independent energy equipment and energy manager.

[0108] S2: Carry out the first phase of the system's load optimization, establish a load transfer mechanism, construct a load optimization problem, and obtain the optimal load demand curve. In this step, a load transfer model for load optimization scheduling needs to be established. Load transfer is performed in both time and space, which can better balance the gap between renewable energy and isolated microgrids. After the load optimization scheduling phase is completed, an optimal load demand curve will be obtained as the load demand input for the second phase.

[0109] The further refinement process of step S2 includes:

[0110] S21: First, obtain the photovoltaic power generation curve and wind power generation curve predicted by each isolated island microgrid, as well as the power curve of each isolated island microgrid (IMG1, IMG2, ..., IMG N )’s initial electric load demand curve.

[0111] S22: Construct an optimization model for dispatching electric loads, i.e., a load transfer model and an objective function for minimizing the remaining cost of renewable energy in the first phase.

[0112] The initial residual renewable energy is defined as the total power of renewable energy With initial electrical load The difference between the actual power load and the actual remaining renewable energy power in each period is expressed as follows:

[0113]

[0114]

[0115] In the formula, They represent the initial electric load of the i-th island microgrid in period t and the actual electric load after the first stage of electric load optimization; represents the actual surplus renewable energy power of the i-th island microgrid in period t, represents the total renewable energy generation power of the i-th island microgrid in period t; and They represent the electric power transferred out or in from the i-th island microgrid in period t respectively; and They represent the load transfer coefficients of the i-th island microgrid transferred out or in during period t.

[0116] The optimization goal of the first stage is to maximize the consumption of renewable energy and minimize user dissatisfaction and the remaining cost of renewable energy. Therefore, the objective function of the first stage contains two parts: the remaining cost of renewable energy and the cost of user electricity dissatisfaction. Based on this, the first stage objective function that minimizes the remaining cost of renewable energy and the compensation cost of user electricity dissatisfaction is established. , and expressed as follows:

[0117]

[0118] In the formula, represents the first stage electrical load optimization cost; represents the residual cost of renewable energy in period t, It represents the compensation cost of user electricity dissatisfaction during period t; and They represent the unit cost of remaining renewable energy and the unit compensation cost of user electricity dissatisfaction respectively; T is the scheduling period.

[0119] S23: Based on the electric load transfer model and the objective function to be optimized, the residual cost of renewable energy and the actual electric load after the first stage optimization are obtained. The actual electric load in each time period constitutes the optimal electric load demand curve.

[0120] Step S2 is the first-stage strategy of the system's optimal scheduling. Its main purpose is to maximize the absorption of renewable energy and reduce the problem of excessive gap between energy supply and demand at certain times.

[0121] S3: According to the optimal electric load demand curve, the carbon trading mechanism and multi-energy sharing mechanism are introduced in the second stage of energy scheduling, and a distributed algorithm is used to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each device to obtain the scheduling strategy.

[0122] In the second phase, carbon trading and multi-energy sharing mechanisms are introduced, and multiple microgrids achieve self- and global cost optimization by sharing multiple energy sources. That is, the energy flow and mutual sharing of electricity and heat between multiple microgrid entities are considered, incorporated into the carbon market, and carbon trading between multiple entities is carried out, and finally a distributed algorithm is used to obtain the optimal decision variables.

[0123] Specifically, the carbon trading mechanism is expressed by the following formula:

[0124]

[0125] In the formula, Represents the price of carbon trading in the carbon trading market; represents the carbon trading cost, and They represent the free carbon quotas that can be obtained per unit power of natural gas heat-based equipment and the free carbon quotas that can be obtained per unit power of renewable energy. Indicates the carbon dioxide emission coefficient of natural gas combustion; , They represent the thermal power and natural gas consumption of the cogeneration unit of the i-th island microgrid in period t, and They represent the heat generation power and natural gas consumption of the gas boiler of the ith island microgrid in period t. The third item represents the carbon dioxide emissions of the system, and the first two items represent the carbon quota owned by the system. The difference between the two is the amount of carbon trading, which can be positive or negative.

[0126] Establish the second-stage objective function to minimize the economic cost of the interconnected multi-microgrid system , and expressed as follows:

[0127]

[0128] In the formula, represents the energy dispatch cost of the second stage; represents the system natural gas consumption cost, represents the eventual cost reductions of renewable energy; It represents the cost of electricity load optimization in the first stage. The second item is the cost of carbon trading, and the third item is the cost of load optimization in the first stage.

[0129] The use of centralized optimization methods for multiple microgrids requires complete information of each microgrid, which may lead to privacy leakage of microgrids. Therefore, this embodiment adopts a distributed optimization method to solve decision variables. Specifically, according to the photovoltaic power generation power curve and wind power generation power curve predicted by each isolated microgrid, as well as the thermal load demand curve, optimal electric load demand curve and electricity price of each isolated microgrid, a distributed energy optimization algorithm based on alternating direction multiplication is used to solve the exchange of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each device to obtain a scheduling strategy.

[0130] The specific process of solving the decision variables using the distributed energy optimization algorithm based on alternating direction multiplication includes:

[0131] S31: Construct the optimized augmented Lagrangian function corresponding to the second-stage objective function of the interconnected multi-microgrid system; introduce the Lagrangian multiplier and Since the energy sharing balance constraints between all isolated microgrids are multi-coupled, the auxiliary variables electric power sharing value and thermal power sharing value are introduced, and the electric power sharing between multiple isolated microgrids must satisfy , thermal power sharing needs to meet , the optimized augmented Lagrangian function L corresponding to the second-stage objective function of the interconnected multi-microgrid system is expressed as follows:

[0132]

[0133] In the formula, represents the economic cost of the i-th island microgrid; and is the penalty parameter, is the power sharing value between the ith island microgrid and the jth island microgrid in period t, is the power sharing value of the j-th island microgrid and the i-th island microgrid in period t; is the thermal power sharing value between the i-th island microgrid and the j-th island microgrid in period t, is the thermal power sharing value of the jth island microgrid and the i-th island microgrid in period t; N\i represents the set of island microgrids excluding the i-th island microgrid in the interconnected multi-microgrid system;

[0134] S32: Update auxiliary variables of electric energy sharing and thermal energy sharing; each island microgrid IMG updates its own energy sharing strategy through local calculations between IMGs, and IMGs only exchange information about energy sharing; the specific update strategies for electric power sharing values ​​and thermal power sharing values ​​are as follows:

[0135]

[0136] In the formula, is the power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration, The thermal power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration; , The Lagrangian calculation functions representing the electric power sharing value and the thermal power sharing value are expressed as follows:

[0137]

[0138]

[0139] According to the results of the electric power sharing value and thermal power sharing value of the i-th island microgrid, the electric power sharing value and thermal power sharing value of the j-th island microgrid at the k+1th iteration are updated. The specific update strategy is as follows:

[0140]

[0141] In the formula, is the power sharing value of the jth island microgrid and the i-th island microgrid in period t at the k+1th iteration, The thermal energy sharing value between the jth island microgrid and the ith island microgrid in period t at the k+1th iteration;

[0142] S33: Update the Lagrange multiplier; according to the updated values ​​of the electric energy and thermal energy sharing values, the Lagrange multiplier is updated in sequence, and its specific mathematical expression is as follows:

[0143]

[0144]

[0145] In the formula, , represents the Lagrange multiplier at the k+1th iteration;

[0146] S34: Repeat steps S32 to S33 until the iteration is terminated to obtain the optimal decision variable value corresponding to the objective function.

[0147] In this embodiment, the method for determining the termination of iteration is: after each round of iteration, the convergence rule is determined, and if the convergence rule is satisfied, the iteration is terminated. The specific convergence rule is as follows:

[0148]

[0149] or,

[0150] k > kmax

[0151] In the formula, and is the convergence threshold, kmax is the maximum number of iterations.

[0152] S4: Real-time scheduling of interconnected multi-microgrid systems based on scheduling strategies.

[0153] First, the load optimization of the first phase is completed. On this basis, the optimization scheduling of the second phase is carried out to obtain the optimal solution to achieve real-time scheduling of the system. The load optimization of the first phase is optimized by each isolated microgrid IMG in its own local energy manager, and the optimization solver is used to solve it. In the second phase, based on the load optimization of the first phase, the electric load optimized in the first phase is used as the load demand input of the second phase. This stage is the stage of distributed optimization scheduling of the multi-microgrid system. The whole system has a coordinator, which serves as a central unit for each isolated microgrid IMG to exchange a small amount of information. Therefore, each isolated microgrid IMG does not need to exchange all its own information, ensuring their privacy.

[0154] The above embodiment provides an isolated multi-microgrid multi-energy sharing scheduling method, which includes a two-stage scheduling strategy, and can overcome the problem that a large number of traditional renewable energy sources are connected to the microgrid, resulting in the inability to maximize absorption and waste. At the same time, the inclusion of mutual sharing of electric energy and thermal energy among multiple microgrids improves the efficient utilization and coordination of energy and reduces the social operating cost of the system. Constructing and designing an energy equipment model for an interconnected multi-microgrid system and a model for optimizing the scheduling of electric loads in the first stage is conducive to designing an efficient energy management scheduling strategy. Introducing a carbon trading model, when the system has a surplus of carbon quotas, it is sold to the market for profit, and when the system is short of carbon quotas, carbon quotas are purchased from the market. Finally, a scheduling strategy for joint optimization of carbon trading and multi-energy sharing is formed, and a distributed solution model based on the alternating direction multiplier method is designed to solve the system's target optimization problem, obtain the optimal decision variable value, and achieve efficient and economical operation scheduling, improve the operational stability of the multi-microgrid system, and achieve the goal of economic and low-carbon social operation.

[0155] The embodiment of the present invention also provides an isolated island multi-microgrid multi-energy sharing scheduling system, such as Figure 2 As shown, including:

[0156] An interconnected multi-microgrid system including multiple isolated microgrids contains multiple forms of energy, which can flow between multiple isolated microgrids. N isolated microgrids are interconnected, all of which are connected to natural gas plants, and form a carbon trading market between each other; each isolated microgrid contains a combined heat and power (CHP), a gas boiler (GB), an energy storage system (ES), a renewable energy (RES), and a local energy manager; all isolated microgrids are managed and controlled by a global coordinator to exchange information on the mutual sharing of electric energy and thermal energy, as well as the transmission and exchange of a small amount of information, so that a global consensus can be reached. The energy flow further includes: electric energy flow, thermal energy flow, and natural gas flow; the electric energy generated by renewable energy and cogeneration equipment flows to the electric load, and the thermal energy generated by cogeneration equipment and gas boilers flows to the thermal load. The information flow flows and interacts in each energy device, user load, and local energy manager.

[0157] The energy manager of each isolated microgrid has an isolated microgrid model and energy balance constraints.

[0158] The energy manager of each island microgrid is configured to perform the first phase of the system’s electrical load optimization, establish an electrical load transfer mechanism, construct an electrical load optimization problem, and obtain the optimal electrical load demand curve;

[0159] The coordinator interacts with each isolated microgrid and is configured to execute according to the optimal electric load demand curve, introduce carbon trading mechanism and multi-energy sharing mechanism in the second stage of energy scheduling, and use distributed algorithm to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each equipment, obtain the scheduling strategy, and perform real-time scheduling of the interconnected multi-microgrid system based on the scheduling strategy.

[0160] The multi-energy sharing dispatching system of the isolated multi-microgrid also includes a measurement system: smart meters, sensors, and storage monitoring systems. Smart meters read natural gas price information, carbon trading price information in the carbon trading market, and renewable energy surplus price information; sensors are used to obtain local information on the electrical load, thermal load, and renewable energy of the multi-microgrid system; and the storage monitoring system measures the battery status of the spring energy storage system.

[0161] In this dispatching system, the energy manager is used to control the load optimization dispatching and obtain the local optimal load curve; the coordinator assists the distributed algorithm and only exchanges a small amount of information in the coordinator to better protect privacy. Finally, the interconnected multi-microgrid system reaches a consensus and obtains the charging / discharging power of the energy storage equipment; the power generation plan of the cogeneration CHP and the gas boiler GB; the interaction of electric energy and thermal energy between multiple isolated microgrid entities; and the carbon trading volume with the carbon trading market.

[0162] The above embodiment provides a multi-energy sharing dispatching system for isolated multi-microgrids, which further narrows the gap between renewable energy and load through electric load optimization, and further improves the utilization efficiency of renewable energy. Considering the carbon trading market, a carbon trading market is formed between multiple microgrid entities, and the carbon trading mechanism is used to reduce carbon emissions. At the same time, the multi-energy mutual assistance and sharing dispatching strategy of electric energy and thermal energy is combined to improve the operating efficiency and stability of the multi-microgrid system, and achieve the win-win goal of economic environment and low-carbon economic operation of the system.

[0163] An embodiment of the present invention further provides an electronic terminal, including:

[0164] Memory on which computer programs or instructions are stored;

[0165] The processor is used to load and execute the computer program or instruction to implement the isolated island multi-microgrid multi-energy sharing scheduling method described in the above embodiment.

[0166] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the isolated island multi-microgrid multi-energy sharing scheduling method described in the above embodiment is implemented.

[0167] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-energy sharing scheduling method for multi-microgrids on isolated islands, characterized in that: The steps include: S1: Modeling an interconnected multi-microgrid system, which contains multiple forms of energy that can flow between multiple isolated microgrids; S2: Carry out the first phase of electric load optimization, establish an electric load transfer mechanism, construct an electric load optimization problem, and obtain the optimal electric load demand curve; S3: According to the optimal electric load demand curve, the carbon trading mechanism and multi-energy sharing mechanism are introduced in the energy scheduling of the second stage, and a distributed algorithm is used to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each device to obtain the scheduling strategy; S4: Real-time scheduling of interconnected multi-microgrid systems based on scheduling strategies; Step S2 specifically includes: Obtain the predicted photovoltaic power generation curve and wind power generation curve of each isolated island microgrid, as well as the initial electric load demand curve of each isolated island microgrid; The electric load transfer model is established and expressed as follows: ； ； In the formula, They represent the initial electric load of the i-th island microgrid in period t and the actual electric load after the first stage of electric load optimization; represents the actual surplus renewable energy power of the i-th island microgrid in period t, represents the total renewable energy generation power of the i-th island microgrid in period t; and They represent the electric power transferred out or in from the i-th island microgrid in period t respectively; and They represent the load transfer coefficients of the i-th island microgrid transferred out or in during period t; Establish the first-stage objective function of minimizing the residual cost of renewable energy and the compensation cost of user electricity dissatisfaction , and expressed as follows: ； In the formula, represents the first stage electrical load optimization cost; represents the residual cost of renewable energy in period t, It represents the compensation cost of user electricity dissatisfaction during period t; and They represent the unit cost of remaining renewable energy and the unit compensation cost of user electricity dissatisfaction respectively; T is the dispatch period; The optimal electric load demand curve is obtained based on the electric load transfer model and the objective function solution; In step S3, the carbon trading mechanism is expressed by the following formula: ； In the formula, Represents the price of carbon trading in the carbon trading market; represents the carbon trading cost, and They represent the free carbon quotas that can be obtained per unit power of natural gas heat-based equipment and the free carbon quotas that can be obtained per unit power of renewable energy. Indicates the carbon dioxide emission coefficient of natural gas combustion; , They represent the thermal power and natural gas consumption of the cogeneration unit of the i-th island microgrid in period t, and They represent the heat generation power and natural gas consumption of the gas boiler of the i-th island microgrid in period t, respectively; T is the scheduling period; Establish the second-stage objective function to minimize the economic cost of the interconnected multi-microgrid system , and expressed as follows: ； In the formula, represents the energy dispatch cost of the second stage; represents the system natural gas consumption cost, represents the eventual cost reductions of renewable energy; represents the first-stage electric load optimization cost; N is the number of isolated microgrids in the interconnected multi-microgrid system; A distributed energy optimization algorithm based on alternating direction multiplication is used to solve the exchange of electric and thermal energy between multiple isolated island microgrids and decide the power generation power of each device to obtain the scheduling strategy.

2. The multi-energy sharing scheduling method for multi-microgrids on isolated islands according to claim 1 is characterized in that: Each isolated microgrid in the interconnected multi-microgrid system includes a combined heat and power (CHP), a gas boiler (GB), an energy storage system (ES), and renewable energy (RES); each isolated microgrid in the interconnected multi-microgrid system is connected to a natural gas plant and contains three types of energy: electricity, natural gas, and thermal energy; the isolated microgrid includes two types of loads: electrical load and thermal load.

3. The multi-energy sharing scheduling method for isolated multi-microgrids according to claim 1 or 2 is characterized in that: The modeling of the interconnected multi-microgrid system includes constructing each isolated island microgrid model and energy supply balance constraints; Among them, each isolated microgrid model includes a combined heat and power CHP model, a gas boiler GB model, an energy storage system ES model, and a renewable energy RES model; The combined heat and power (CHP) model is expressed as follows: ； ； In the formula, , , They represent the power generation power, thermal power and natural gas consumption of the cogeneration unit of the i-th island microgrid in period t respectively; , They represent the electricity generation efficiency and heat generation efficiency of the cogeneration of the i-th island microgrid respectively; is the set of scheduling periods, N is the set of isolated microgrids in the interconnected multi-microgrid system; The GB model of gas boiler is expressed as follows: ； In the formula, represents the combustion efficiency of the gas boiler in the i-th island microgrid, and They represent the heating power and natural gas consumption of the gas boiler of the i-th island microgrid in period t respectively; The ES model of the energy storage system is expressed as follows: ； In the formula, and They represent the energy storage status of the energy storage system of the i-th island microgrid in period t and period t-1 respectively; and They represent the charging power and discharging power of the energy storage system of the i-th island microgrid in period t respectively; and They represent the charging efficiency and discharging efficiency of the energy storage system of the i-th island microgrid respectively; Indicates the duration of each scheduling period; The renewable energy RES model is expressed as follows: ； ； ； In the formula, and They represent the photovoltaic power generation and wind power generation of the i-th island microgrid in period t respectively; and They represent the maximum power of photovoltaic power generation and wind power generation of the i-th island microgrid respectively; represents the total renewable energy generation power of the i-th island microgrid in period t; The energy balance constraint is expressed as follows: ； ； In the formula, and They represent the electric load and thermal load of the i-th island microgrid in period t respectively.

4. The multi-energy sharing scheduling method for isolated multi-microgrids according to claim 1 is characterized in that: The distributed energy optimization algorithm based on alternating direction multiplication is used to solve the exchange amount of electric energy and thermal energy between multiple isolated island microgrids and decide the power generation power of each device, specifically including: S31: Construct the optimized augmented Lagrangian function corresponding to the second-stage objective function of the interconnected multi-microgrid system; introduce the Lagrangian multiplier and , and consider that the power sharing between multiple isolated microgrids must meet , thermal power sharing needs to meet , the optimized augmented Lagrangian function L corresponding to the second-stage objective function of the interconnected multi-microgrid system is expressed as follows: ； In the formula, represents the economic cost of the i-th island microgrid; and is the penalty parameter, is the power sharing value between the ith island microgrid and the jth island microgrid in period t, is the power sharing value of the j-th island microgrid and the i-th island microgrid in period t; is the thermal power sharing value between the i-th island microgrid and the j-th island microgrid in period t, is the thermal power sharing value of the jth island microgrid and the i-th island microgrid in period t; N\i represents the set of island microgrids excluding the i-th island microgrid in the interconnected multi-microgrid system; S32: Each island microgrid updates its own energy sharing strategy through local calculations between island microgrids. The island microgrids only exchange information about energy sharing. The specific update strategies for the electric power sharing value and the thermal power sharing value are as follows: ； In the formula, is the power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration, The thermal power sharing value between the ith island microgrid and the jth island microgrid in period t at the k+1th iteration; , Lagrangian calculation functions representing the electric power sharing value and the thermal power sharing value respectively; According to the results of the electric power sharing value and thermal power sharing value of the i-th island microgrid, the electric power sharing value and thermal power sharing value of the j-th island microgrid at the k+1th iteration are updated. The specific update strategy is as follows: ； In the formula, is the power sharing value of the jth island microgrid and the i-th island microgrid in period t at the k+1th iteration, The thermal energy sharing value between the jth island microgrid and the ith island microgrid in period t at the k+1th iteration; S33: Update the Lagrange multiplier; according to the updated values ​​of the electric energy and thermal energy sharing values, the Lagrange multiplier is updated in sequence, and its specific mathematical expression is as follows: ； ； In the formula, , represents the Lagrange multiplier at the k+1th iteration; S34: Repeat steps S32 to S33 until the iteration is terminated to obtain the optimal decision variable value corresponding to the objective function.

5. The multi-energy sharing scheduling method for isolated multi-microgrids according to claim 2 is characterized in that: The renewable energy in each isolated microgrid in the interconnected multi-microgrid system includes at least one of wind energy and solar energy.

6. An isolated island multi-microgrid multi-energy sharing scheduling system for implementing the isolated island multi-microgrid multi-energy sharing scheduling method as claimed in claim 1, characterized in that: include: An interconnected multi-microgrid system including multiple isolated microgrids, wherein the interconnected multi-microgrid system includes multiple forms of energy, and the multiple forms of energy can flow between multiple isolated microgrids; The energy manager of each isolated microgrid has an isolated microgrid model and energy balance constraints. The energy manager of each island microgrid is configured to perform the first phase of the system’s electrical load optimization, establish an electrical load transfer mechanism, construct an electrical load optimization problem, and obtain the optimal electrical load demand curve; The coordinator interacts with each isolated microgrid and is configured to execute according to the optimal electric load demand curve, introduce carbon trading mechanism and multi-energy sharing mechanism in the second stage of energy scheduling, and use distributed algorithm to solve the sharing value of electric energy and thermal energy between multiple isolated microgrids and decide the power generation power of each equipment, obtain the scheduling strategy, and perform real-time scheduling of the interconnected multi-microgrid system based on the scheduling strategy.

7. An electronic terminal, characterized in that: include: Memory on which computer programs or instructions are stored; A processor is used to load and execute the computer program or instruction to implement the isolated island multi-microgrid multi-energy sharing scheduling method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by the processor, the isolated island multi-microgrid multi-energy sharing scheduling method as described in any one of claims 1 to 5 is implemented.

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