Optimal operation method of regional micro-energy grid considering maximum absorption of renewable energy

Abstract

The present invention discloses a method for optimizing the operation of a regional micro-energy network considering the maximum absorption of renewable energy, including S1: inputting user-side load demand; S2: establishing a model of energy supply-side equipment; S3: establishing a micro-energy network system model; S4: establishing a linearized system model; the linearized system model is divided into two sub-layers, an optimization layer and an operation layer; this method for optimizing the operation of a regional micro-energy network considering the maximum absorption of renewable energy is different from the prior art. The present invention, on the basis of considering the volatility and randomness of regional wind and solar resources, establishes a system model and an optimization model of a wind, solar, fuel and storage regional micro-energy network based on the characteristics of different types of loads of local users, and designs a two-layer configuration optimization logic, with the goal of maximizing wind and solar penetration and minimizing wind and solar abandonment rates. Through mixed integer nonlinear programming solution, it can effectively increase the absorption of wind and solar power, and provide configuration planning, response plans, load distribution suggestion support, etc. for users and plant areas.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization and scheduling, and in particular to a method for optimizing the operation of a regional micro-energy network taking into account the maximum absorption of renewable energy. Background Art

[0002] The patent "Dispatching Optimization Method for Multi-Energy Coupling Integrated Energy System" provides a dispatching optimization method for a multi-energy coupling regional micro-energy network. According to the characteristics of energy and time scale in the regional micro-energy network, the differences in the dispatching time scale of electricity, cold, heat, and gas multi-energy flows are considered, and a day-ahead dispatching plan and intra-day rolling optimization dispatching are proposed to eliminate the random volatility of renewable energy and loads, and achieve stable and economic operation. The preliminary dispatching plan obtained by the day-ahead dispatching uses intra-day stratified rolling re-optimization to timely and effectively correct the power fluctuations caused by wind, light, and load forecast errors, realize the correction of the system's day-ahead plan, ensure stable and economic operation between various energy sources, achieve system supply and demand balance, and reduce operating costs.

[0003] The patent "A method for optimizing the operation of a segmented integrated energy system based on mixed integer nonlinear programming" provides a multi-objective day-ahead optimization scheduling method that takes into account voltage and pressure control. First, according to the segmented electricity price of "valley-peak-flat-peak-flat", 24 hours are divided into five periods, and the optimal optimization variable matrix of the five periods is calculated through mixed integer nonlinear programming, and then the objective function and constraints of each period are constructed respectively, and finally the optimization results of each period are calculated in turn through mixed integer nonlinear programming. This method can simplify the number of model constraints as much as possible, realize the optimal configuration of the operation of multiple energy sources in the regional micro-energy network, and achieve the maximum economic benefit.

[0004] Patent "A Regional Integrated Energy System Planning Optimization Method Based on Double-layer Optimization" is a regional micro-energy network planning optimization method based on double-layer optimization. Aiming at the characteristics of wind energy and energy storage in regional micro-energy networks, an optimization scheduling model suitable for regional micro-energy networks is established, which converts random problems into deterministic problems. Under the condition of ensuring system safety, an effective optimization scheduling implementation method is provided for the optimization scheduling of regional micro-energy networks. Compared with the traditional regional micro-energy network optimization scheduling method, the operation plan obtained by the optimization scheduling model can be flexibly adjusted under different wind power output scenarios, which improves the economy of the comprehensive system, and also reduces the impact of wind power output volatility on the power grid, with better stability and reliability.

[0005] The above patents have studied the operation optimization of micro-energy grid systems, but most of them aim to minimize the operating cost. There is no in-depth research on how to increase the proportion of renewable energy in the energy structure and fully absorb it. With the development of regional micro-energy grids, large-scale renewable energy equipment is connected to the system, and its volatility and randomness have brought challenges to the supply and demand matching of the energy system. Only by increasing the penetration ratio of renewable energy in the energy system, reducing unnecessary wind and solar abandonment, and exploring the balanced interaction of wind, solar, fuel storage, and cold, heat and electricity in quality and energy flow, can the low-carbon, clean and sustainable effects of regional micro-energy grids be truly achieved. Therefore, it is very important to optimize the system operation considering the maximum absorption of renewable energy. To this end, we propose a regional micro-energy grid operation optimization method considering the maximum absorption of renewable energy. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the operation of a regional micro-energy grid taking into account the maximum absorption of renewable energy, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for optimizing the operation of a regional micro-energy network considering the maximum consumption of renewable energy, comprising the following steps:

[0008] S1: Input user-side load demand;

[0009] S2: Establish a model of the energy supply equipment;

[0010] S3: Establish a micro energy grid system model;

[0011] According to the model established in step S2, the configuration capacity of key components of the micro energy grid system is used as a decision variable, and operation optimization is carried out based on mixed integer nonlinear programming;

[0012] The optimization problem of the micro energy grid system is expressed as follows:

[0013]

[0014] According to the above optimization problem, the objective function to be optimized is determined as:

[0015]

[0016]

[0017]

[0018] In the formula, REP (Renewable Energy Penetration) refers to the wind and solar penetration rate, CR (Curtailment Rate) refers to the wind and solar abandonment rate, and Wwind,used , W solar,used Refers to the portion of user electricity consumption that comes from wind and solar power generation, W gas,used , W grid,used Refers to the portion of user electricity consumption that comes from gas turbines and power purchased from the main power grid. wasted Solar wasted is the power wasted by wind power and photovoltaic power generation in time period T due to energy storage capacity limitations and other reasons. By combining the two indicators according to weighted analysis, we can get the optimization function that considers the maximum absorption of renewable energy.

[0019] According to the above optimization objective function, the optimization scheduling constraints are established:

[0020] (1) Inequality constraints:

[0021] Aineq×[x]≤bineq

[0022] The inequality constraints considered in this optimization mainly include:

[0023]

[0024]

[0025]

[0026] The constraints in the above formula represent: the operating capacity of the prime mover is not greater than the rated capacity and not less than (N-1) times the rated capacity, where: E0 represents the rated capacity, N represents the number of prime movers selected; the steam produced by the waste heat boiler should be less than the maximum steam production of the prime mover flue gas heat carrying, where: Qgas represents the prime mover flue gas heat carrying, ηHRSG represents the waste heat boiler efficiency, HHRSG represents the waste heat boiler steam production; the steam required by the bromide refrigeration machine is not greater than the total steam produced by the waste heat boiler and the gas boiler, where: Hgas_boil represents the steam production of the gas boiler, QLibr represents the bromide refrigeration machine capacity, and COP represents the refrigeration performance coefficient of the bromide refrigeration machine.

[0027] The energy storage charging and discharging power cannot exceed the energy storage rated power:

[0028] P in , P out <P cs,N

[0029] Energy storage battery capacity constraints:

[0030] The amount of electricity in the energy storage battery at each time should be between the upper and lower limits of the battery power constraints:

[0031] S lower,lmit t N ​​

[0032] Energy storage rate constraints:

[0033] P cs,N =S N / k

[0034] Where k is the energy rate of the energy storage battery.

[0035] (2) Equality constraints

[0036] Photovoltaic power generation equation constraints

[0037] P pv =A pv *F pv *(1-0.005(T-25))*η pv

[0038] Power balance constraints

[0039]

[0040] Q Libr +Q chill =Q user

[0041] H HRSG +H gas_boil =H user +H Libr

[0042] The above formulas respectively represent the balance and matching between system output and user's cooling, heating and electricity.

[0043] Where SN is the rated capacity of the energy storage battery, P cs,N Rated power for energy storage configuration, To buy electrical power, is the existing transformer capacity, is the newly added transformer capacity, c is the discharge rate, P pv is the photovoltaic power, is the discharge power; is the charging power, is the Internet access power, P load is the load power, F pv is the photovoltaic irradiation, T is the ambient temperature η pv Photovoltaic efficiency, δ is a value of 0 or 1, ε is a minimum value, M is a maximum value, Qgas represents the heat carried by the flue gas of the prime mover, ηHRSG represents the efficiency of the waste heat boiler, HHRSG represents the steam output of the waste heat boiler, Hgas_boil represents the steam output of the gas boiler, QLibr represents the capacity of the bromide chiller, COP represents the refrigeration performance coefficient of the bromide chiller, Qchill represents the cooling capacity of the electric refrigeration unit, and COPchill represents the electric refrigeration performance coefficient.

[0044] S4: Establish linearized system model;

[0045] The linearized system model is divided into two sub-layers, the optimization layer and the operation layer;

[0046] (1) Optimization layer

[0047] The decision variables and related constraints of the micro-energy grid system model in step S3 are iteratively solved through the MINLP solver, and the number of samples is generated. The OPTI toolbox is called based on the Matlab platform, and the mixed integer nonlinear programming problem is solved through the BONMIN algorithm to realize the operation optimization algorithm.

[0048] (2) Operation layer

[0049] For each sample combination generated by the optimization layer, the operation layer will perform calculations in turn. Through calculations, the operation layer can obtain the renewable penetration rate and wind and solar power abandonment rate of the system during the operating time. The operation layer will perform the next iteration based on the returned data, and automatically change the operating parameters until the optimal solution set is obtained.

[0050] Preferably, the user terminals described in step S1 include residential buildings, residential buildings and new energy vehicles.

[0051] Preferably, the energy supply end equipment described in step S2 includes a small wind turbine, a solar power generation unit, an energy storage battery, a gas turbine, a bromide refrigeration unit, and the like.

[0052] Preferably, the samples described in step S3 include wind power installed capacity, photovoltaic installed power, number of gas turbines, waste heat boiler capacity, lithium bromide refrigerant capacity, electric refrigeration capacity and energy storage element capacity, etc.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention is different from the prior art. On the basis of considering the volatility and randomness of regional wind and solar resources and based on the characteristics of different types of loads of local users, the present invention establishes a system model and optimization model of a regional micro-energy network of wind, solar, fuel and storage, and designs a two-layer configuration optimization logic. With the goal of maximizing the wind and solar penetration rate and minimizing the wind and solar abandonment rate, the mixed integer nonlinear programming solution can effectively increase the wind and solar absorption capacity, and provide configuration planning, response plans, load distribution recommendation support, etc. for users and plant areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of the present invention;

[0056] Figure 2 It is an operation block diagram of the present invention;

[0057] Figure 3 This is a structural diagram of the regional micro energy network of the present invention;

[0058] Figure 4 It is a table diagram of the operating conditions of each component under three loads of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1-4 The present invention provides a technical solution: a method for optimizing the operation of a regional micro-energy network considering the maximum consumption of renewable energy, comprising the following steps:

[0061] S1: Input user-side load demand;

[0062] S2: Establish a model of the energy supply equipment;

[0063] S3: Establish a micro energy grid system model;

[0064] According to the model established in step S2, the configuration capacity of key components of the micro energy grid system is used as a decision variable, and operation optimization is carried out based on mixed integer nonlinear programming;

[0065] The optimization problem of the micro energy grid system is expressed as follows:

[0066]

[0067] According to the above optimization problem, the objective function to be optimized is determined as:

[0068]

[0069]

[0070]

[0071] In the formula, REP (Renewable Energy Penetration) refers to the wind and solar penetration rate, CR (Curtailment Rate) refers to the wind and solar abandonment rate, and W wind,used , W solar,used Refers to the portion of user electricity consumption that comes from wind and solar power generation, W gas,used , W grid,usedRefers to the portion of user electricity consumption that comes from gas turbines and power purchased from the main power grid. wasted Solar wasted is the power wasted by wind power and photovoltaic power generation in time period T due to energy storage capacity limitations and other reasons. By combining the two indicators according to weighted analysis, we can get the optimization function that considers the maximum absorption of renewable energy.

[0072] According to the above optimization objective function, the optimization scheduling constraints are established:

[0073] (1) Inequality constraints:

[0074] Aineq×[x]≤bineq

[0075] The inequality constraints considered in this optimization mainly include:

[0076]

[0077]

[0078]

[0079] The constraints in the above formula represent: the operating capacity of the prime mover is not greater than the rated capacity and not less than (N-1) times the rated capacity, where: E0 represents the rated capacity, N represents the number of prime movers selected; the steam produced by the waste heat boiler should be less than the maximum steam production of the prime mover flue gas heat carrying, where: Qgas represents the prime mover flue gas heat carrying, ηHRSG represents the waste heat boiler efficiency, HHRSG represents the waste heat boiler steam production; the steam required by the bromide refrigeration machine is not greater than the total steam produced by the waste heat boiler and the gas boiler, where: Hgas_boil represents the steam production of the gas boiler, QLibr represents the bromide refrigeration machine capacity, and COP represents the refrigeration performance coefficient of the bromide refrigeration machine.

[0080] The energy storage charging and discharging power cannot exceed the energy storage rated power:

[0081] P in , P out <P cs,N

[0082] Energy storage battery capacity constraints:

[0083] The amount of electricity in the energy storage battery at each time should be between the upper and lower limits of the battery power constraints:

[0084] S lower,lmit t N

[0085] Energy storage rate constraints:

[0086] P cs,N =S​​N / k

[0087] Where k is the energy rate of the energy storage battery.

[0088] (2) Equality constraints

[0089] Photovoltaic power generation equation constraints

[0090] P pv =A pv *F pv *(1-0.005(T-25))*η pv

[0091] Power balance constraints

[0092]

[0093] Q Libr +Q chill =Q user

[0094] H HRSG +H gas_boil =H user +H Libr

[0095] The above formulas respectively represent the balance and matching between system output and user's cooling, heating and electricity.

[0096] Where SN is the rated capacity of the energy storage battery, P cs,N Rated power for energy storage configuration, To buy electrical power, is the existing transformer capacity, is the newly added transformer capacity, c is the discharge rate, P pv is the photovoltaic power, is the discharge power; is the charging power, is the Internet access power, P load is the load power, F pv is the photovoltaic irradiation, T is the ambient temperature η pv Photovoltaic efficiency, δ is a value of 0 or 1, ε is a minimum value, M is a maximum value, Qgas represents the heat carried by the flue gas of the prime mover, ηHRSG represents the efficiency of the waste heat boiler, HHRSG represents the steam output of the waste heat boiler, Hgas_boil represents the steam output of the gas boiler, QLibr represents the capacity of the bromide chiller, COP represents the refrigeration performance coefficient of the bromide chiller, Qchill represents the cooling capacity of the electric refrigeration unit, and COPchill represents the electric refrigeration performance coefficient.

[0097] S4: Establish linearized system model;

[0098] The linearized system model is divided into two sub-layers, the optimization layer and the operation layer;

[0099] (1) Optimization layer

[0100] The decision variables and related constraints of the micro-energy grid system model in step S3 are iteratively solved by the MINLP solver, and the number of samples is generated. The OPTI toolbox is called based on the Matlab platform, and the mixed integer nonlinear programming problem is solved by the BONMIN algorithm to implement the running optimization algorithm. BONMIN is an algorithm and software for convex mixed integer nonlinear programs of the local MINLP solver. It is a branch-and-bound algorithm based on LP / NLP and is used to deal with convex MINLP optimization problems. The branch-and-bound algorithm is a direct generalization of the branch-and-bound algorithm for solving MILP in the MINLP problem. The algorithm has three key steps: branching, node selection, and pruning.

[0101] (2) Operation layer

[0102] For each sample combination generated by the optimization layer, the operation layer will perform calculations in turn. Through calculations, the operation layer can obtain the renewable penetration rate and wind and solar power abandonment rate of the system during the operating time. The operation layer will perform the next iteration based on the returned data, and automatically change the operating parameters until the optimal solution set is obtained.

[0103] Furthermore, the user terminals described in step S1 include residential buildings, residential buildings, and new energy vehicles, etc.

[0104] Furthermore, the energy supply end equipment described in step S2 includes a small wind turbine, a solar power generation, an energy storage battery, a gas turbine and a bromide refrigeration machine, etc.

[0105] Furthermore, the samples described in step S3 include wind power installed capacity, photovoltaic installed power, number of gas turbines, waste heat boiler capacity, lithium bromide refrigerant capacity, electric refrigeration capacity and energy storage element capacity, etc.

[0106] Select three groups of typical loads to test the optimization results of the comprehensive energy system: The three groups of typical loads are shown in the following table.

[0107] Table 1: Load conditions

[0108] Electric load kW Heat load kW Cooling load kW Transition Season 5900 2100 1500 summer 7200 700 6150 winter 6085 5950 390

[0109] in:

[0110] Case-A is the spring load in March, including electric load of 5900kW, thermal load of 2100kW and cooling load of 1500kW.

[0111] Case-B is the summer load in July, including electric load 7200kW, thermal load 700kW and cooling load 6150kW.

[0112] Case-C is the winter load in November, including electric load 6085kW, thermal load 5950kW and cooling load 390kW.

[0113] The comprehensive energy system configuration in the example includes: 1 200kW gas turbine, 5 1000kW gas turbines; 2 5000kW waste heat boilers; 2 4000kW absorption bromine chillers; 1 500kW electric refrigeration equipment, 1 1600kW photovoltaic power generation system consisting of 5000 unit panels; 10 500kW wind turbines; 1 1000kW power storage device; 1 1000kW cold storage device. At the same time, the micro energy network can purchase electricity from the power grid. In order to achieve the purpose of local consumption of wind and solar resources, the purchase of electricity is limited to no more than 10% of the load. In the optimization process, the decision variable is the power output of each component of the system, and the optimization variable is the renewable energy consumption index considering the wind and solar penetration rate and the abandonment rate.

[0114] Table 2: Micro energy network configuration table

[0115]

[0116] After operation optimization, the output of each component is:

[0117] 1) Transitional season operation:

[0118] Electric load: 4 1000kW gas turbines are selected to run at full load, with an output power of 4000kW, and 1 1000kW gas turbine has an output power of 478kW; photovoltaic power generation is 912kW, wind power generation is 1200 kW; and 303kW of electricity is purchased from the power grid. Thermal load: 1 5000kW waste heat boiler is selected to run, with an output power of 3434kW. Cold load: 1 4000kW lithium bromide refrigerator is selected to run, with an output power of 1500kW.

[0119] After operation optimization, the system's wind and solar penetration rate is 34.54%, and the wind and solar abandonment rate is 0.

[0120] 2) Summer operation:

[0121] Electric load: 4 1000kW gas turbines are selected to operate at full load, with an output power of 4000kW, and 1 1000kW gas turbine has an output power of 861kW; photovoltaic power generation is 1358kW, wind power generation is 1200kW; and 543kW of electricity is purchased from the power grid. Thermal load: 2 5000kW waste heat boilers are selected to operate, with an output power of 3000kW for one and 2560kW for the other. Cold load: 2 4000kW lithium bromide refrigerators are selected to operate, with an output power of 4000kW for one at full load and 2150kW for the other.

[0122] After operation optimization, the system's wind and solar penetration rate is 33.04%, and the wind and solar abandonment rate is 0%.

[0123] 3) Winter operation:

[0124] Electric load: 5 1000kW gas turbines are selected to run at full load, with an output power of 5000kW, and 1 200kW gas turbine has an output power of 119kW; photovoltaic power generation is 800kW, wind power generation is 500kW; and another 535kW of electricity is purchased from the power grid.

[0125] Heat load: Two 5000kW waste heat boilers are selected for operation, one with a full load output of 5000kW and the other with an output of 1228kW. Cold load: One 4000kW lithium bromide refrigerator is selected with an output of 390kW.

[0126] After operation optimization, the system's wind and solar penetration rate is 18.71%, and the wind and solar abandonment rate is 0%.

[0127] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0128] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional micro-energy grid operation optimization method considering the maximum consumption of renewable energy, characterized in that: The steps include: S1: Input user-side load demand; S2: Establish a model of the energy supply equipment; S3: Establish a micro energy grid system model; According to the model established in step S2, the configuration capacity of key components of the micro energy grid system is used as a decision variable, and operation optimization is carried out based on mixed integer nonlinear programming; The optimization problem of the micro energy grid system is expressed as follows: According to the above optimization problem, the objective function to be optimized is determined as: In the formula, REP (Renewable Energy Penetration) refers to the wind and solar penetration rate, CR (Curtailment Rate) refers to the wind and solar abandonment rate, and W wind,used , W solar,used Refers to the portion of user electricity consumption that comes from wind and solar power generation, W gas,used , W grid,used Refers to the portion of user electricity consumption that comes from gas turbines and power purchased from the main power grid. wasted Solar wasted is the power wasted by wind power and photovoltaic power generation in time period T due to the limitation of energy storage capacity. By combining the two indicators according to weighting, the optimization function considering the maximum absorption of renewable energy can be obtained; According to the above optimization objective function, the optimization scheduling constraints are established: (1) Inequality constraints: Aineq×[x]≤bineq The inequality constraints considered in this optimization mainly include: The constraints in the above formula represent: the operating capacity of the prime mover is not greater than the rated capacity and not less than (N-1) times the rated capacity, where: E0 represents the rated capacity, N represents the number of prime movers selected; the steam produced by the waste heat boiler should be less than the maximum steam production of the prime mover flue gas heat, where: Qgas represents the prime mover flue gas heat, ηHRSG represents the waste heat boiler efficiency, HHRSG represents the waste heat boiler steam production; the steam required by the bromide refrigeration machine is not greater than the total steam produced by the waste heat boiler and the gas boiler, where: Hgas_boil represents the steam production of the gas boiler, QLibr represents the bromide refrigeration machine capacity, and COP represents the refrigeration performance coefficient of the bromide refrigeration machine; The energy storage charging and discharging power cannot exceed the energy storage rated power: P in ,P out <P cs,N Energy storage battery capacity constraints: The amount of electricity in the energy storage battery at each time should be between the upper and lower limits of the battery power constraints: S lower,lmit <S t <S N Energy storage rate constraints: P cs,N =S N / k In the formula, k is the energy rate of the energy storage battery; (2) Equality constraints Photovoltaic power generation equation constraints P pv =A pv *F pv *(1-0.005(T-25))*η pv Power balance constraints Q Libr +Q chill =Q user H HRSG +H gas_boil =H user +H Libr The above formulas respectively represent the balance and matching between the system output and the user's cooling, heating and electricity; Where SN is the rated capacity of the energy storage battery, P cs,N The rated power configured for energy storage; P is the purchased power; pv is the photovoltaic power, is the charging power, is the Internet access power, P load is the load power; F pv is photovoltaic irradiation, T is ambient temperature, η pv Photovoltaic efficiency; HHRSG represents the steam output of the waste heat boiler, Hgas_boil represents the steam output of the gas boiler, QLibr represents the capacity of the bromide refrigeration unit, COP represents the refrigeration performance coefficient of the bromide refrigeration unit, Qchill represents the cooling capacity of the electric refrigeration unit, and COPchill represents the electric refrigeration performance coefficient; S4: Establish linearized system model; The linearized system model is divided into two sub-layers, the optimization layer and the operation layer; (1) Optimization layer The decision variables and related constraints of the micro-energy grid system model in step S3 are iteratively solved by the MINLP solver, and the number of samples is generated. The OPTI toolbox is called based on the Matlab platform, and the mixed integer nonlinear programming problem is solved by the BONMIN algorithm to implement the running optimization algorithm. (2) Operation layer For each sample combination generated by the optimization layer, the operation layer will perform calculations in turn. Through calculations, the operation layer can obtain the renewable penetration rate and wind and solar power abandonment rate of the system during the operating time. The operation layer will perform the next iteration based on the returned data, and automatically change the operating parameters until the optimal solution set is obtained.

2. The regional micro-energy grid operation optimization method considering the maximum consumption of renewable energy according to claim 1 is characterized in that: The user terminals described in step S1 include residential buildings, residential buildings and new energy vehicles.

3. The regional micro-energy grid operation optimization method considering the maximum consumption of renewable energy according to claim 1 is characterized in that: The energy supply equipment described in step S2 includes a small wind turbine, a solar power generation, an energy storage battery, a gas turbine and a bromide refrigeration machine.

4. The regional micro-energy grid operation optimization method considering the maximum consumption of renewable energy according to claim 1 is characterized in that: The samples described in step S3 include wind power installed capacity, photovoltaic installed power, number of gas turbines, waste heat boiler capacity, lithium bromide refrigerant capacity, electric refrigeration capacity and energy storage element capacity.

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