Micro-grid distributed multi-objective optimization scheduling method

Abstract

The invention provides a distributed multi-objective optimization scheduling method for a micro-grid, and the method comprises the following steps: 1, carrying out the main region division based on the difference of the source-load-storage main interests in an electrical network-micro-grid, and constructing a micro-grid system framework; 2, establishing a distributed multi-target optimization scheduling model of the micro-grid system based on the micro-grid system framework constructed in the step 1; and step 3, based on a Pareto optimal principle, designing a distributed multi-objective optimization scheduling algorithm, and solving a micro-grid multi-objective optimization problem. According to the distributed optimization scheduling method based on the Pareto optimal principle, the multi-target optimization problem of the micro-grid can be converted into the single-target distributed optimization problem to be solved, the system operation efficiency is improved, and the economic operation cost of the micro-grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid energy optimization scheduling, and in particular to a microgrid distributed multi-objective optimization scheduling method. Background Art

[0002] With the needs of human development, the exploitation of non-renewable energy has been increasing, leading to increasingly serious environmental pollution and energy supply shortages. Therefore, energy and environmental issues have always been the focus of international attention. Renewable energy generation technology has become an important means for many countries to solve energy crises and environmental pollution problems with its advantages such as cleanliness and environmental protection. However, due to the strong intermittency, large random fluctuations, and weak controllability of renewable energy (such as wind energy, solar energy, etc.), it is urgent to develop new optimization control systems or methods to solve the above problems. Microgrid is a small-scale independent system that uses modern power electronics technology to connect distributed power sources such as micro gas turbines, wind turbines, photovoltaic power generation systems, fuel cells, and energy storage devices, and access appropriate cold / heat / electric loads.

[0003] In the operation of the microgrid system, in addition to ensuring the reliable and stable operation of the system, the economic efficiency of the system operation must also be considered. In the actual operation process, the following operating costs need to be considered: 1) Power purchase cost, that is, the cost of purchasing power from the large power grid when the power generation of the microgrid system is not enough for the local load; 2) Loss cost, after purchasing power from the large power grid, it needs to be transmitted through the transmission line and provided to the local load for use. Line loss will inevitably occur during the transmission process; 3) Power generation cost, including the power generation cost of traditional diesel generators, the power generation cost of photovoltaic power generation units, the cost of wind turbines, the power generation cost of micro-turbines, and the energy storage cost of batteries; 4) Environmental cost, mainly including the related costs of carbon emissions and environmental pollution. Obviously, the economic dispatch problem of the microgrid system is a typical multi-objective optimization problem. However, the existing single-objective distributed optimization algorithm cannot solve the above multi-objective optimization problems at the same time.

[0004] There are many patents that describe the optimization scheduling method of microgrids, such as the application number CN202011400607.0, the invention name is "A hierarchical distributed optimization scheduling method for interconnected microgrids"; the application number CN202111275523.3, the invention name is "A distributed optimization control method and device for off-grid DC microgrids". These invention patents use distributed optimization methods to optimize the scheduling and control of microgrids, but do not consider the various operating costs of the system. There are also some patents that study the multi-objective optimization problem of microgrids. For example, the application number CN201710797494.4, the invention name is "Microgrid multi-objective operation optimization method based on PSO improved algorithm"; the application number CN202310710553.5, the invention name is "A microgrid multi-objective optimization method and system based on improved snake optimization algorithm". However, the optimization algorithms of the above invention patents are essentially centralized algorithm architectures, resulting in large computational complexity and unguaranteed convergence. Therefore, it is urgent to design a distributed multi-objective optimization method to solve the multi-objective optimization problem of microgrids. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed multi-objective optimization scheduling method for a microgrid, which mainly solves the multi-objective distributed optimization scheduling problem of a microgrid. Specifically, it includes:

[0006] 1) Distributed processing of multi-objective optimization scheduling problems of microgrids can solve the problems of large computational complexity, poor robustness, large communication volume, and weak plug-and-play performance of traditional centralized multi-objective optimization methods;

[0007] 2) A multi-objective distributed optimization method is used to solve the multi-objective optimization scheduling problem of microgrids, which reduces the complexity of the optimization scheduling problem and improves the optimization efficiency.

[0008] The method steps provided in this application are as follows:

[0009] Step 1: Construct a microgrid optimization control framework. The framework consists of an electrical grid, a controllable power generation unit, an environmental layer, a load layer, and related power electronic modules. The electrical grid only accepts user power purchase demand information and supplies electricity to the microgrid. The microgrid transmits data information to each other through neighbors, and optimizes the neighbors and its own energy-consuming equipment and energy-supplying equipment. The equipment is reasonably adjusted through the intelligent terminal to achieve self-optimization.

[0010] Step 2: Establish a distributed optimization dispatch model for the microgrid system. According to the constructed microgrid optimization control framework, the controllable resource areas in the microgrid mainly include: diesel generators, photovoltaic power generation units, wind power generation models, micro-turbine modules, batteries, flexible loads, etc. By performing distributed coordinated control of controllable resources in the microgrid, a distributed optimization dispatch model is established to save system operating costs and improve system operating efficiency.

[0011] Step 3: Design a distributed multi-objective optimization scheduling method suitable for the multi-objective optimization problem of microgrids. Considering the complexity of the coupling between adjacent microgrid devices and electrical equipment, directly solving the multi-objective problem will be very complicated and require huge computing resources. This paper designs a distributed coordination optimization method based on the multi-agent system model. The advantage of this method is that it combines the existing single-objective distributed optimization algorithm with the decomposition principle and solves the Pareto optimal solution of the multi-objective distributed optimization problem of microgrids.

[0012] Further, step 1 comprises:

[0013] The microgrid system framework includes the electrical grid layer, the controllable power generation unit layer, the load layer and the environmental layer; the electrical grid layer supplies electricity and gas energy to the microgrid system according to the user's electricity demand and generates corresponding electricity purchase costs; the controllable power generation unit layer includes diesel generators, photovoltaic generators, wind turbines, micro gas turbines and battery modules, which are power generation units in the microgrid system; the environmental layer is used to reduce carbon emissions, including P2G units and CSS units; the load layer is mainly the user end.

[0014] Further, step 2 includes:

[0015] The overall operating cost of the microgrid includes three parts: the cost of gas and electricity purchased from the power grid, the power generation cost of the controllable power generation unit, and the environmental protection cost; set the global optimization variable z = col (z1, z2, z3), where z1 = (P Grid ,Q Grid ) T , z2=(P DG,i ,P PV,i ,P WT,i ,P GT,i ,P SB,i ) T , z3=(P CCS ,P P2G ) T ;

[0016] (1) The objective function of electricity purchase cost is expressed as:

[0017] F1(z)=C Grid +C loss (1)

[0018] Among them, C Grid The cost of purchasing electricity from the upper power grid is C loss is the network electrical loss cost; the power purchase cost objective function of the microgrid system includes:

[0019] Cost model for purchasing electricity from the upper power grid:

[0020]

[0021] In formula (2), C Grid The real-time cost of purchasing electricity from the grid, is the unit price of electricity purchased at different times, is the amount of electricity purchased from the power grid at time t, is the gas purchase price at different times, is the amount of gas purchased from the power grid at time t, Δt is the unit time;

[0022] Network electrical loss cost model:

[0023]

[0024] Among them, C loss is the network electrical loss cost, λ is the power loss coefficient, P t is the equipment power at time t, χ is the thermal loss coefficient, Q t It represents the natural gas loss dispatched in the gas grid at time t, and Δt is the unit time;

[0025] (2) The overall cost function of the controllable power generation unit is as follows:

[0026] F2(z2)=C DG +C WT +C PV +C GT +C SB (4)

[0027] Among them C DG is the total diesel generator power generation cost function in the system, C WT is the total wind turbine power generation cost function, C PV is the solar power generation cost function, C GT is the power generation cost function of the micro-turbine, C SB is the cost function of the energy storage module; the details are as follows:

[0028] Diesel generator power generation cost function:

[0029]

[0030] Among them, C DG is the diesel generator cost, NDG is the number of diesel generators in the microgrid, is the unit power generation cost of the diesel generator, is the diesel generator power, Δt is the unit time;

[0031] Photovoltaic generator power generation cost function:

[0032]

[0033] Among them, C PV is the dispatching cost of photovoltaic generators, N PV is the number of photovoltaic generators in the microgrid, is the real-time power of the photovoltaic generator, is the rated power of the photovoltaic generator, is the unit dispatching cost of the photovoltaic generator, Δt is the unit time;

[0034] Wind turbine power generation cost function:

[0035]

[0036] Among them, C WT is the dispatching cost of wind turbines, N WT is the number of wind turbines in the microgrid, is the real-time power of the wind turbine, is the rated power of the wind turbine, is the unit dispatching cost of wind turbines, Δt is the unit time;

[0037] Micro turbine power generation cost function:

[0038]

[0039] Among them, C GT is the power generation cost of micro-turbine, N GT is the number of micro turbines in the microgrid, is the unit power generation cost of the micro-turbine, is the power generated by the micro-turbine, Δt is the unit time;

[0040] Battery charging and discharging cost function:

[0041]

[0042] Among them, C SB is the battery dispatch cost, N SB is the number of batteries in the microgrid, is the battery unit dispatch cost, are the battery charging and discharging power respectively, Δt is the unit time;

[0043] The battery plays an important role in ensuring the stable operation of the microgrid. The charging and discharging model of the energy storage system is as follows:

[0044]

[0045] SOC min ≤SOC(t)≤SOC max

[0046]

[0047] SOC(0)=SOC(24)

[0048] Among them, formula (16) is the battery charging and discharging power constraint, the first formula in formula (17) is the battery energy constraint, and the second and third formulas in formula (17) ensure the battery next day power equation constraint; The upper and lower limits of battery charge and discharge power, SOC max , SOC min is the upper and lower limits of battery power, η c , η disc It is the battery charging and discharging efficiency;

[0049] (3) The environmental cost objective function is set as:

[0050] F3(z3)=C EP (18)

[0051] A carbon capture unit CCS is built to capture CO2 produced by thermal power generators; in addition, a P2G unit is used to synthesize natural gas to make full use of carbon resources; the power change process during the operation of CCS and P2G is as follows:

[0052] CCS operation process:

[0053]

[0054]

[0055]

[0056]

[0057] P2G operation process:

[0058]

[0059]

[0060]

[0061]

[0062] Among them, CCS captures micro-turbine emissions In η CCS is the capture efficiency, and the CCS capture amount is obtained The amount of CCS captured transported to P2G and the excess stored; the amount of natural gas produced by P2G is is the amount of CO2 purchased, γ and δ are the P2G conversion efficiency and CO2 emission efficiency respectively; (25)(26) are the P2G unit output and ramp constraints respectively;

[0063] The environmental protection cost objective function is:

[0064]

[0065] Among them, σ is the penalty for excess CO2 emissions, is the amount of CO2 captured by the CSS unit, is the amount of CO2 produced by the micro-turbine, is the upper limit of carbon emissions, Δt is the unit time;

[0066] (4) The global coupling constraint is set as follows:

[0067] The constraints of the microgrid system optimization dispatch model are as follows:

[0068]

[0069]

[0070]

[0071]

[0072] Among them, equations (28)(29) are the power balance conditions and gas balance conditions respectively, and equations (30)(31) are the power purchase and gas purchase constraint conditions respectively;

[0073] Based on the electricity purchase cost model, the controllable power generation unit power generation cost model and the environmental protection cost model, the following multi-objective power generation cost function is established:

[0074]

[0075] Among them, the total optimization variable x=z=col(x i ) i＝1,..,N =(P Grid ,Q Grid ,P DG,i ,...,P P2G )T ∈R N , where N is the total number of energy supply units in this microgrid; where A i x i =B i represents the equality constraints of (28)-(29), g i (x i )≤0 represents the inequality constraints of (16)-(17), (25)-(26), and (30)-(31).

[0076] Further, step 3 includes:

[0077] Based on the Pareto optimality principle, the multi-objective optimization problem (32) is transformed into the following single-objective optimization problem (33):

[0078]

[0079] Among them, w1+w2+w3=1 and w1,w2,w3>0, x=col{x1,x2,...,x N}, x i is the power generation cost variable of the i-th energy supply unit;

[0080] According to the above optimization goal (33), the following distributed optimization algorithm is designed:

[0081]

[0082] where c ij is the i-th row and j-th column element of the adjacency matrix of the communication network graph, representing the communication relationship between the two energy supply units; They are the converted single-objective optimization problem functions f i (x i ) and the gradient value of the inequality constraint function g(x); y, μ and η are auxiliary variables, which respectively ensure that the algorithm satisfies the equality constraints, inequality constraints and the consistency of the optimization variable x.

[0083] The beneficial effects of the present invention are:

[0084] 1. Establish a microgrid optimization framework, combining the power grid and controllable power generation units. The two complement each other, improving the stability of the power grid operation and the reliability of power supply. At the same time, it has better adaptability and robustness in the face of sudden power and gas supply accidents.

[0085] 2. By setting up local units in each module within the microgrid for information interaction and data processing, distributed optimization scheduling control can be achieved, thereby solving the problems of large computational complexity, complex communication and poor robustness of the traditional centralized scheduling method, and effectively solving the data privacy protection issues of various stakeholders in the microgrid.

[0086] 3. A distributed optimization scheduling method based on the Pareto optimal principle is adopted to transform the multi-objective optimization problem of the microgrid into a single-objective distributed optimization problem for solution, thereby improving the efficiency of system operation and reducing the economic operation cost of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is an overall schematic diagram of the distributed multi-objective optimization scheduling method of the microgrid of the present invention;

[0088] Figure 2 This is the overall framework diagram of the microgrid system of the present invention;

[0089] Figure 3 This is a distributed control structure diagram of the multi-objective optimization scheduling of the microgrid of the present invention;

[0090] Figure 4 It is an iterative flow chart of the multi-objective distributed scheduling algorithm of the microgrid of the present invention. DETAILED DESCRIPTION

[0091] The present invention is further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto.

[0092] A distributed multi-objective optimization scheduling method for microgrids, such as Figure 1 As shown, it mainly includes the following three steps:

[0093] Step 1: Based on the different interests of the main sources, loads and storage in the power grid-microgrid, the main areas are divided and the overall framework of the microgrid system is constructed.

[0094] Step 2: Based on the microgrid system framework constructed in step 1, a distributed multi-objective optimization scheduling model of the microgrid system is established.

[0095] Step 3: Based on the Pareto optimal principle, a distributed multi-objective optimization scheduling algorithm is designed to solve the multi-objective optimization problem of microgrids.

[0096] Specifically, step 1 includes:

[0097] The overall framework of the microgrid system is as follows: Figure 2 As shown in the figure, it mainly includes the electrical grid layer, the controllable power generation unit layer, the load layer and the environmental layer. The electrical grid layer supplies electricity and gas energy to the microgrid system according to the user's electricity demand, and generates corresponding electricity purchase costs; the controllable power generation unit layer mainly includes diesel generators, photovoltaic generators, wind turbines, micro-gas turbines and battery modules, etc., which are the main power generation units in the microgrid system; the environmental layer is used to reduce carbon emissions, including P2G units and CSS units; the load layer is mainly the user end.

[0098] The controllable resource communication network and control structure in the microgrid are as follows: Figure 3 As shown. In each controllable power generation unit module, a local controller is set. The local controller has the main functions of communication, data processing, control, and implementation of distributed optimization algorithms. Through the mutual coordination of adjacent controllable resources in the microgrid, the purpose of ensuring the reliable and stable operation of the system and maximizing the overall economic benefits is achieved.

[0099] Specifically, step 2 includes:

[0100] Depend on Figure 1 As shown in Figure 1, the overall operating cost of the microgrid mainly includes three parts: the cost of gas and electricity purchased from the power grid, the power generation cost of the controllable power generation unit, and the environmental protection cost. Set the global optimization variable z = col(z1,z2,z3), where z1 = (P Grid ,Q Grid ) T , z2=(P DG,i ,P PV,i ,P WT,i ,P GT,i ,P SB,i ) T , z3=(P CCS ,P P2G ) T . Each type of cost will be modeled separately below.

[0101] (1) The objective function of electricity purchase cost is expressed as:

[0102] F1(z)=C Grid +C loss (1)

[0103] Among them, C Grid The cost of purchasing electricity from the upper power grid is C loss This is the cost of network electrical losses.

[0104] Specifically, the electricity purchase cost function of the microgrid system is defined as follows:

[0105] Cost model for purchasing electricity from the upper power grid:

[0106]

[0107] In formula (2), C Grid The real-time cost of purchasing electricity from the grid, is the unit price of electricity purchased at different times, is the amount of electricity purchased from the power grid at time t, C is Grid Different (z1 different) = time pt G carve rid Buy P Gt ri gas d Δt unit + price gt g , r Δ is the amount of gas purchased from the power grid at time tt, and Δt is the unit time.

[0108] Network electrical loss cost model:

[0109]

[0110] Among them, C loss is the network electrical loss cost, λ is the power loss coefficient, P t is the equipment power at time t, χ is the thermal loss coefficient, Q t It represents the natural gas loss dispatched in the gas grid at time t, and Δt is the unit time.

[0111] (2) The overall cost function of the controllable power generation unit is as follows:

[0112] F2(z2)=C DG +C WT +C PV +C GT +C SB (4)

[0113] Among them C DG is the total diesel generator power generation cost function in the system, C WT is the total wind turbine power generation cost function, C PV is the solar power generation cost function, C GT is the power generation cost function of the micro-turbine, C SB is the cost function of the energy storage module. The details are as follows:

[0114] Diesel generator power generation cost function:

[0115]

[0116] Among them, C DG is the diesel generator cost, N DG is the number of diesel generators in the microgrid, is the unit power generation cost of the diesel generator, is the diesel generator power, Δt is the unit time.

[0117] Photovoltaic generator power generation cost function:

[0118]

[0119] Among them, C PV is the dispatching cost of photovoltaic generators, N PVis the number of photovoltaic generators in the microgrid, is the real-time power of the photovoltaic generator, is the rated power of the photovoltaic generator, is the unit dispatching cost of the photovoltaic generator, and Δt is the unit time. The power generation of the photovoltaic generator is described below.

[0120] It is known that photovoltaic power generation is greatly affected by climate and region. The output of photovoltaic panels is related to radiation intensity and temperature. The Beta distribution probability density function is:

[0121]

[0122] Among them, Q, Q max are the light intensity and maximum light intensity in the time period respectively; α and β are Beta distribution parameters. The average light intensity and standard deviation obtained from the Beta probability distribution can be obtained:

[0123]

[0124] Among them, T, T ST are the photovoltaic cell node temperature and the standard reference temperature of 25 degrees Celsius, and k is the temperature coefficient.

[0125] Wind turbine power generation cost function:

[0126]

[0127] Among them, C WT is the dispatching cost of wind turbines, N WT is the number of wind turbines in the microgrid, is the real-time power of the wind turbine, is the rated power of the wind turbine, is the unit dispatching cost of the wind turbine, and Δt is the unit time. The power generation of the wind turbine is described below.

[0128] It is known that wind power generation is similar to photovoltaic power generation and is greatly affected by climate and region, so a wind speed probability model is often constructed to analyze wind turbines. Here, the Weibull distribution probability model is used:

[0129]

[0130] Among them, w is the wind speed, q and c (q>1, c>1) are the wind turbine parameters.

[0131] The relationship between wind speed and fan is as follows:

[0132]

[0133] Among them, win 、w n 、w out are cut-in wind speed, rated wind speed, cut-out wind speed, a WT , b WT 、c WT is the wind speed power parameter, P n is the rated power of the wind turbine.

[0134] Micro turbine power generation cost function:

[0135]

[0136] Among them, C GT is the power generation cost of micro-turbine, N GT is the number of micro turbines in the microgrid, is the unit power generation cost of the micro-turbine, is the power generated by the micro-turbine, and Δt is the unit time. The power generated by the micro-turbine is described below.

[0137] Micro gas turbines generate electricity by consuming natural gas. The amount of natural gas consumed and the power generated have the following relationship:

[0138] P GT =F gas L HV η GT (13)

[0139]

[0140] Among them, F gas is the amount of natural gas consumed, η GT is the power generation efficiency, L HV is the calorific value of natural gas, is the rated power of the micro-turbine, and a1, a2, a3, a4, and a5 are fixed parameters of the micro-turbine.

[0141] Battery charging and discharging cost function:

[0142]

[0143] Among them, C SB is the battery dispatch cost, N SB is the number of batteries in the microgrid, is the battery unit dispatch cost, are the battery charging and discharging power respectively, and Δt is the unit time.

[0144] The battery plays an important role in ensuring the stable operation of the microgrid. The charging and discharging model of the energy storage system is as follows:

[0145]

[0146] SOC min ≤SOC(t)≤SOC max

[0147]

[0148] SOC(0)=SOC(24)

[0149] Among them, formula (16) is the battery charging and discharging power constraint, the first formula in formula (17) is the battery energy constraint, and the second and third formulas in formula (17) ensure the battery next day power equation constraint; The upper and lower limits of battery charge and discharge power, SOC max , SOC min is the upper and lower limits of battery power, η c , η disc It is the battery charging and discharging efficiency.

[0150] (3) The environmental cost objective function is set as:

[0151] F3(z3)=C EP (18)

[0152] In order to recycle the carbon elements in the microgrid system, a carbon capture unit (CCS) is built to capture the CO2 produced by the thermal power generator; in addition, the P2G unit is used to synthesize natural gas to make full use of carbon resources. The power change process during the operation of CCS and P2G is as follows:

[0153] CCS operation process:

[0154]

[0155]

[0156]

[0157]

[0158] P2G operation process:

[0159]

[0160]

[0161]

[0162]

[0163] Among them, CCS captures micro-turbine emissions In η CCS is the capture efficiency, and the CCS capture amount is obtained The amount of CCS captured The amount of natural gas produced by P2G is is the amount of CO2 purchased, γ and δ are the P2G conversion efficiency and CO2 emission efficiency respectively; (25)(26) are the P2G unit output and ramp constraints respectively.

[0164] The environmental protection cost objective function is:

[0165]

[0166] Among them, σ is the penalty for excess CO2 emissions, is the amount of CO2 captured by the CSS unit, is the amount of CO2 produced by the micro-turbine, is the upper limit of carbon emissions, and Δt is the unit time.

[0167] (4) The global coupling constraint settings are as follows:

[0168] Specifically, the constraints of the microgrid system optimization scheduling model are as follows:

[0169]

[0170]

[0171]

[0172]

[0173] Among them, equations (28)(29) are the power balance conditions and gas balance conditions respectively, and equations (30)(31) are the power purchase and gas purchase constraint conditions respectively.

[0174] Therefore, based on the power purchase cost model, the controllable power generation unit power generation cost model and the environmental protection cost model, the following multi-objective power generation cost function is established:

[0175]

[0176] Among them, the total optimization variable x=z=col(x i ) i＝1,..,N =(P Grid ,Q Grid ,P DG,i ,...,P P2G ) T ∈R N , where N is the total number of energy supply units in this microgrid; where A ix i =B i represents the equality constraints of (28)-(29), g i (x i )≤0 represents the inequality constraints of (16)-(17), (25)-(26), and (30)-(31).

[0177] Specifically, step 3 includes:

[0178] Obviously, the multi-objective optimization (32) (power purchase cost, controllable power generation unit power generation cost, environmental protection cost) problem is global and multiple objectives are coupled together. In order to reduce the design complexity of the algorithm, based on the Pareto optimality principle, the multi-objective optimization problem (32) is converted into the following single-objective optimization problem (33):

[0179]

[0180] Among them, w1+w2+w3=1 and w1,w2,w3>0, x=col{x1,x2,...,x N}, x i is the power generation cost variable of the i-th energy supply unit.

[0181] In view of the above optimization goal (33), the present invention designs the following distributed optimization algorithm:

[0182]

[0183] where c ij is the i-th row and j-th column element of the adjacency matrix of the communication network graph, representing the communication relationship between the two energy supply units; They are the converted single-objective optimization problem functions f i (x i ) and the gradient value of the inequality constraint function g(x); y, μ and η are auxiliary variables, which respectively ensure that the algorithm satisfies the equality constraints, inequality constraints and the consistency of the optimization variable x.

[0184] The above algorithm is in continuous time form. In order to be used in the scenario of assembling a small microcontroller, the above algorithm can be discretized. The specific expression is shown in Table 1.

[0185] Table 1 Pseudo code of multi-objective distributed optimization scheduling algorithm for microgrid

[0186]

[0187]

[0188] Finally, the execution flow chart of the distributed multi-objective optimization algorithm of the microgrid is given as follows: Figure 4 shown.

[0189] The above is only a preferred embodiment of the present invention, which is used to illustrate the technical solution of the present invention, but not to limit the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, some of the technical features therein are modified and replaced, and they will not depart from the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A distributed multi-objective optimization scheduling method for a microgrid, characterized in that: The following steps are involved: Step 1: Based on the different interests of the main sources, loads and storage in the power grid-microgrid, the main areas are divided and the microgrid system framework is constructed; Step 2: Based on the microgrid system framework constructed in step 1, a distributed multi-objective optimization scheduling model of the microgrid system is established; Step 3: Based on the Pareto optimal principle, a distributed multi-objective optimization scheduling algorithm is designed to solve the multi-objective optimization problem of microgrids.

2. A distributed multi-objective optimization scheduling method for microgrids according to claim 1, characterized in that: Step 1 includes: The microgrid system framework includes the electrical grid layer, the controllable power generation unit layer, the load layer and the environmental layer; the electrical grid layer supplies electricity and gas energy to the microgrid system according to the user's electricity demand and generates corresponding electricity purchase costs; the controllable power generation unit layer includes diesel generators, photovoltaic generators, wind turbines, micro gas turbines and battery modules, which are power generation units in the microgrid system; the environmental layer is used to reduce carbon emissions, including P2G units and CSS units; the load layer is mainly the user end.

3. A distributed multi-objective optimization scheduling method for microgrids according to claim 2, characterized in that: Step 2 includes: The overall operating cost of the microgrid includes three parts: the cost of gas and electricity purchased from the power grid, the power generation cost of the controllable power generation unit, and the environmental protection cost; set the global optimization variable z = col (z1, z2, z3), where z1 = (P Grid ,Q Grid ) T , z2=(P DG,i ,P PV,i ,P WT,i ,P GT,i ,P SB,i ) T , z3=(P CCS ,P P2G ) T ; (1) The objective function of electricity purchase cost is expressed as: F1(z)=C Grid +C loss (1) Among them, C Grid The cost of purchasing electricity from the upper power grid is C loss is the network electrical loss cost; the power purchase cost objective function of the microgrid system includes: Cost model for purchasing electricity from the upper power grid: In formula (2), C Grid The real-time cost of purchasing electricity from the grid, is the unit price of electricity purchased at different times, is the amount of electricity purchased from the power grid at time t, is the gas purchase price at different times, is the amount of gas purchased from the power grid at time t, Δt is the unit time; Network electrical loss cost model: Among them, C loss is the network electrical loss cost, λ is the power loss coefficient, P t is the equipment power at time t, χ is the thermal loss coefficient, Q t It represents the natural gas loss dispatched in the gas grid at time t, and Δt is the unit time; (2) The overall cost function of the controllable power generation unit is as follows: F2(z2)=C DG +C WT +C PV +C GT +C SB (4) Among them C DG is the total diesel generator power generation cost function in the system, C WT is the total wind turbine power generation cost function, C PV is the solar power generation cost function, C GT is the power generation cost function of the micro-turbine, C SB is the cost function of the energy storage module; the details are as follows: Diesel generator power generation cost function: Among them, C DG is the diesel generator cost, N DG is the number of diesel generators in the microgrid, is the unit power generation cost of the diesel generator, is the diesel generator power, Δt is the unit time; Photovoltaic generator power generation cost function: Among them, C PV is the dispatching cost of photovoltaic generators, N PV is the number of photovoltaic generators in the microgrid, is the real-time power of the photovoltaic generator, is the rated power of the photovoltaic generator, is the unit dispatching cost of the photovoltaic generator, Δt is the unit time; Wind turbine power generation cost function: Among them, C WT is the wind turbine dispatch cost, N WT is the number of wind turbines in the microgrid, is the real-time power of the wind turbine, is the rated power of the wind turbine, is the unit dispatching cost of wind turbines, Δt is the unit time; Micro turbine power generation cost function: Among them, C GT is the power generation cost of micro-turbine, N GT is the number of micro turbines in the microgrid, is the unit power generation cost of the micro-turbine, is the power generated by the micro-turbine, Δt is the unit time; Battery charging and discharging cost function: Among them, C SB is the battery dispatch cost, N SB is the number of batteries in the microgrid, is the battery unit dispatch cost, are the battery charging and discharging power respectively, Δt is the unit time; The battery plays an important role in ensuring the stable operation of the microgrid. The charging and discharging model of the energy storage system is as follows: Among them, formula (16) is the battery charging and discharging power constraint, the first formula in formula (17) is the battery energy constraint, and the second and third formulas in formula (17) ensure the battery next day power equation constraint; The upper and lower limits of battery charge and discharge power, SOC max , SOC min is the upper and lower limits of battery power, η c , η disc It is the battery charging and discharging efficiency; (3) The environmental cost objective function is set as: <h2 style=";text-align:left;direction:ltr">F3(z3)=C<h2 style=";text-align:left;direction:ltr"> EP <h2 style=";text-align:left;direction:ltr"> (18) A carbon capture unit CCS is built to capture CO2 produced by thermal power generators; in addition, a P2G unit is used to synthesize natural gas to make full use of carbon resources; the power change process during the operation of CCS and P2G is as follows: CCS operation process: P2G operation process: Among them, CCS captures micro-turbine emissions In η CCS is the capture efficiency, and the CCS capture amount is obtained The amount of CCS captured transported to P2G and the excess stored; the amount of natural gas produced by P2G is is the amount of CO2 purchased, γ and δ are the P2G conversion efficiency and CO2 emission efficiency respectively; (25)(26) are the P2G unit output and ramp constraints respectively; The environmental protection cost objective function is: Among them, σ is the penalty for excess CO2 emissions, is the amount of CO2 captured by the CSS unit, is the amount of CO2 produced by the micro-turbine, is the upper limit of carbon emissions, Δt is the unit time; (4) The global coupling constraint is set as follows: The constraints of the microgrid system optimization dispatch model are as follows: Among them, equations (28)(29) are the power balance conditions and gas balance conditions respectively, and equations (30)(31) are the power purchase and gas purchase constraint conditions respectively; Based on the electricity purchase cost model, the controllable power generation unit power generation cost model and the environmental protection cost model, the following multi-objective power generation cost function is established: Among them, the total optimization variable x=z=col(x i ) i＝1,..,N =(P Grid ,Q Grid ,P DG,i ,...,P P2G ) T ∈R N , where N is the total number of energy supply units in this microgrid; where A i x i =B i represents the equality constraints of (28)-(29), g i (x i )≤0 represents the inequality constraints of (16)-(17), (25)-(26), and (30)-(31).

4. A distributed multi-objective optimization scheduling method for microgrids according to claim 3, characterized in that: Step 3 includes: Based on the Pareto optimality principle, the multi-objective optimization problem (32) is transformed into the following single-objective optimization problem (33): Among them, w1+w2+w3=1 and w1,w2,w3>0, x=col{x1,x2,...,x N }, x i is the power generation cost variable of the i-th energy supply unit; According to the above optimization goal (33), the following distributed optimization algorithm is designed: where c ij is the i-th row and j-th column element of the adjacency matrix of the communication network graph, representing the communication relationship between the two energy supply units; They are the converted single-objective optimization problem functions f i (x i ) and the gradient value of the inequality constraint function g(x); y, μ and η are auxiliary variables, which respectively ensure that the algorithm satisfies the equality constraints, inequality constraints and the consistency of the optimization variable x.

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