Micro-grid optimization operation method considering different operation modes

Abstract

The invention relates to a micro-grid optimization operation method considering different operation modes, and belongs to the technical field of micro-grids. According to the method, based on a micro-grid operation mode and interaction electric quantity of a micro-grid and a large power grid, a first objective function is constructed by taking interaction cost minimization of the micro-grid and the large power grid as an objective; based on the rated power of each unit in the micro-grid and the gas consumption of the gas turbine, constructing a second target function by taking the operation cost minimization as a target; based on the gas turbine power generation power, the total power generation amount of the wind turbine generator, the total power generation amount of the photovoltaic unit, and the charging power and the discharging power of the energy storage unit, constructing a third objective function by taking micro-grid income maximization as an objective; and carrying out joint solution on the plurality of objective functions based on set constraints to obtain a micro-grid optimization operation scheme. According to the method, the utilization rate of new energy output in the micro-grid can be effectively improved, the adjustment capability of flexible resources such as energy storage in the micro-grid is fully exerted, and the operation cost of the micro-grid is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrids, and in particular relates to a microgrid optimization operation method considering different operation modes. Background Art

[0002] As an advanced technical means and grid form, microgrid is an effective way to promote the consumption of new energy, improve energy efficiency and reduce energy costs. However, due to the obvious uncertainty and volatility of the distributed power output within the microgrid, the microgrid currently in operation and construction lacks a reasonable and standardized operation and management model, and cannot effectively deal with uncertainty and volatility, nor can it fully utilize the flexible adjustment function of energy storage equipment. In the case of large-scale access, there may be problems such as reverse overload, wind and solar abandonment, resulting in energy waste, and at the same time posing challenges to the operation and control of microgrids and large grids. Summary of the invention

[0003] In view of the above analysis, the present invention aims to provide a microgrid optimization operation method taking into account different operating modes. By considering the independent operation mode and grid-connected operation mode of the microgrid, and the wind and solar uncertainty constraints of the microgrid, with the goal of minimizing the operating cost and maximizing the benefit, the microgrid optimization operation plan is solved to promote the consumption of new energy, improve energy utilization, and ensure the safe and stable operation of the microgrid and the large power grid.

[0004] The method of the present invention comprises the following steps:

[0005] Based on the microgrid operation mode and the interactive power between the microgrid and the large power grid, a first objective function is constructed with the goal of minimizing the interactive cost between the microgrid and the large power grid; the microgrid operation mode includes an independent operation mode and a grid-connected operation mode;

[0006] Based on the rated power of each unit in the microgrid and the gas consumption of all gas turbines in the microgrid, the second objective function is constructed with the goal of minimizing the operating cost of the microgrid;

[0007] Based on the power generation of gas turbines, the total power generation of wind turbines, the total power generation of photovoltaic units, the charging power and the discharging power of energy storage units in the microgrid, the third objective function is constructed with the goal of maximizing the benefits of the microgrid;

[0008] Based on the microgrid self-balancing constraints, power balance constraints, energy storage unit constraints, power generation constraints and wind and solar uncertainty constraints, the first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan, including the microgrid operation mode in each period, the interactive power between the microgrid and the large power grid, the gas turbine power generation, and the charging power and discharging power of the energy storage unit.

[0009] Furthermore, the microgrid self-balancing constraints include:

[0010] The annual interactive power between the microgrid and the large grid does not exceed the first preset proportion of the annual power consumption; the interactive power between the microgrid and the large grid includes the power sent from the large grid to the microgrid and the power sent from the microgrid to the large grid; the annual power consumption of the microgrid includes the annual power consumption of the microgrid users;

[0011] In the independent operation mode, the microgrid self-balancing constraint further includes:

[0012] The sum of the outputs of each unit in the microgrid satisfies the continuous power supply of the load in the microgrid for no less than two hours; the output of each unit includes wind power output, photovoltaic output, energy storage discharge power and gas turbine power generation power, and the load power supply in the microgrid includes the power load of microgrid users and energy storage charging power;

[0013] In the grid-connected operation mode, the microgrid self-balancing constraint further includes:

[0014] The microgrid has a demand-side response capability that is not less than a second preset proportion of the maximum power load. Further, the first preset proportion is 50%, and the microgrid self-balancing constraints include:

[0015] Where ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t The t period is the amount of electricity sent from the large grid to the microgrid, and the amount of electricity sent from the microgrid to the large grid; Δt is the time interval; T is the total number of time periods throughout the year, that is, the total duration of the year; P load,t is the total load power during period t, P CL,t is the normal load power, is the transferable load after the transfer in period t, is the load that can be reduced after reduction in period t;

[0016] In the independent operation mode, the microgrid self-balancing constraints also include:

[0017]

[0018] Among them, P w (t), P p (t), P dis (t), P c (t), P ch (t) are wind power output, photovoltaic output, energy storage discharge power, gas turbine power generation power and energy storage charging power in period t; t0 is the initial period of the microgrid independent operation mode;

[0019] In the grid-connected operation mode, the second preset proportion is 10%, and the microgrid self-balancing constraint also includes:

[0020]

[0021] in, and They are respectively the upper limit of the amount that can be transferred and the upper limit of the amount that can be reduced during period t.

[0022] Furthermore, the wind and solar uncertainty constraints are determined based on the following steps:

[0023] Construct the probability density function of wind power and photovoltaic output based on Gaussian kernel function;

[0024] The wind-solar combined output distribution function is obtained based on the probability density function of wind power and photovoltaic output through the Frank-Copula function;

[0025] The new energy scenario matrix is ​​obtained by random sampling based on the wind-solar combined output distribution function;

[0026] Based on the clustering of new energy scenario matrix, the set of typical new energy output scenarios and the probability of each typical output scenario are obtained;

[0027] Determine wind and solar uncertainty constraints based on typical output scenarios.

[0028] Furthermore, based on the microgrid operation mode and the interactive power between the microgrid and the large grid, the first objective function is constructed with the goal of minimizing the interactive cost between the microgrid and the large grid as expressed as:

[0029]

[0030] Where F1 represents the first objective function; s i represents the i-th typical scenario; N s represents the number of typical scenes; ω(s i ) is a typical scenario i The probability of γ in-out is the electricity interaction cost coefficient; ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t The time period t is the amount of electricity sent from the large grid to the microgrid, and the amount of electricity sent from the microgrid to the large grid; Δt is the time interval.

[0031] Furthermore, based on the rated power of each unit in the microgrid and the gas consumption of all gas turbines in the microgrid, the second objective function is constructed with the goal of minimizing the operating cost of the microgrid as expressed as:

[0032]

[0033] Where F2 represents the second objective function; θ is the microgrid component that needs to be maintained; Ω θ A collection of microgrid components that require maintenance, including wind turbines, photovoltaic units, gas turbines, and energy storage units; θ is the annual unit operating cost of component θ; is the rated power of component θ; γ GT is the unit fuel cost; P CGT,t is the gas consumption of all gas turbines during period t; θ TL and γ RL are the unit demand response costs of the load that can be transferred and the load that can be reduced; ΔP TL,t and ΔP RL,t are the load transfer and load reduction in period t respectively.

[0034] Furthermore, the third objective function is constructed based on the gas consumption of the gas turbine in the microgrid, the total power generation of the wind turbine, the total power generation of the photovoltaic unit, the charging power and the discharging power of the energy storage unit, with the goal of maximizing the benefit of the microgrid, and is expressed as:

[0035]

[0036] Where F3 represents the third objective function; f c is the net income of gas turbine; f w is the net income of the wind turbine; f p all is the net income of the photovoltaic unit; f ES is the net income of the energy storage unit;

[0037] pc in (t) is the peak-valley time-of-use electricity price within the microgrid during period t; Q in c (t) is the electricity sold by the gas turbine in the microgrid during period t; E GT (t) is the power generation of the gas turbine during period t; (aE 2 GT (t)+bE GT (t)+c) is the amount of natural gas consumed by the gas turbine during period t; a, b and c are the natural gas consumption coefficients of the gas turbine respectively; P G (t) is the unit price of natural gas during period t;

[0038] Q in w (t) is the electricity sales of wind turbines in the microgrid during period t; pc b w Subsidize the price of wind power. is the total power generation of the wind turbine, where t=1,2,...24; pc w is the operating cost per unit of electricity generated by the wind turbine;

[0039] Q in p (t) is the electricity sales of the PV unit within the microgrid during period t; is the total power generation of the photovoltaic unit, where t=1,2,...24; pc b p is the photovoltaic electricity subsidy price; pc p is the operating cost per unit of electricity generated by the PV unit;

[0040] η ch is the charging efficiency of the energy storage device; η dis is the discharge efficiency of the energy storage device; P ch (t) is the charging power of the energy storage device; P dis (t) is the discharge power of the energy storage device; δ is a 0-1 variable, when δ is 1, it means that the energy storage is discharging, and when it is 0, it means that it is charging; ρ h is the peak electricity price; ρ l The off-peak electricity price; The amount of electricity sold by the provincial power grid from the self-generation and self-storage facilities of the new energy base; For backup power; It is the reserve compensation price per unit capacity.

[0041] Furthermore, the first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan, including:

[0042] De-dimensionalize the values ​​of the first, second, and third objective functions;

[0043] Assign weights to each objective function to obtain a multi-objective model;

[0044] The multi-objective model is solved to obtain the optimal operation plan of the microgrid, including the microgrid operation mode in each period, the interactive power between the microgrid and the large grid, the gas consumption of the gas turbine, and the charging power and discharging power of the energy storage unit.

[0045] Furthermore, the power balance constraint is expressed as:

[0046]

[0047] in, They are the power consumption rates of photovoltaic units, wind turbines, and a single gas turbine respectively; is the charge and discharge loss of energy storage; Q in (t) is the total amount of electricity sold within the microgrid during period t, Q out(t) is the total amount of electricity sold by the microgrid to the outside during period t.

[0048] Furthermore, the power generation constraint is expressed as:

[0049]

[0050] in, is the total user load in period t; K is the preset percentage value.

[0051] The present invention can achieve at least one of the following beneficial effects:

[0052] By considering the independent operation mode and grid-connected operation mode of the microgrid, considering the conventional load, transferable load and reducible load of the microgrid, constructing the self-balancing constraint of the microgrid, and by considering the uncertainty constraints of wind and solar power, with the goal of minimizing operating costs and maximizing benefits, the optimal operation plan of the microgrid is solved to promote the consumption of new energy, improve energy utilization, and ensure the safe and stable operation of the microgrid and the large power grid.

[0053] By obtaining a set of typical output scenarios of new energy based on historical data analysis, the uncertain output of new energy in the microgrid is accurately characterized, so as to set wind and solar uncertainty constraints. The objective function is solved based on the wind and solar uncertainty constraints, which can effectively deal with the uncertainty of new energy in the microgrid and ensure the stability of power operation in the microgrid.

[0054] The accuracy of the solution is further improved by de-dimensionalizing the multi-objective function and assigning weights to each objective function based on the objective integrated weighting method of hierarchical analysis method and entropy weight method.

[0055] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0057] Figure 1 is a flow chart of the method of the present invention;

[0058] Figure 2 This is a schematic diagram of the microgrid structure of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0060] A specific embodiment of the present invention discloses a microgrid optimization operation method considering different operation modes (such as Figure 1 The microgrid is a small power system, including distributed renewable energy units (wind turbines and photovoltaic units), micro gas turbines, electric energy storage and loads. The microgrid is also connected to the public power grid, i.e., the large power grid. Figure 2 Schematic diagram of the microgrid structure.

[0061] Specifically, distributed wind turbines and distributed photovoltaic units are responsible for supplying power to the overall load of the microgrid; micro gas turbines are used to meet the power supply gap caused by insufficient output of wind and solar units; electric energy storage is used to store the part of wind and solar output that is greater than the load, and charge to make up for the power supply gap when the output of wind and solar units is insufficient. The large power grid is only used for internal and external power transactions in the microgrid.

[0062] Furthermore, in the present invention, in order to maximize the promotion of clean energy consumption, it is assumed that gas turbines and energy storage must cooperate with wind and light clean energy units and cannot operate alone to jointly meet the electricity load needs of users within the microgrid; in order to ensure the stability of energy use and the security of the power grid, it is assumed that wind and light clean energy units cannot operate alone and must cooperate with other stable power generating units to jointly meet the electricity load needs of users within the microgrid.

[0063] The method of the present invention comprises steps S01 to S04.

[0064] Step S01, based on the microgrid operation mode and the interactive power between the microgrid and the large grid, a first objective function is constructed with the goal of minimizing the interaction cost between the microgrid and the large grid; the microgrid operation mode includes an independent operation mode and a grid-connected operation mode.

[0065] Specifically, when the microgrid is in an independent operation mode, the internal load demand is met through cooperation among multiple output entities within the microgrid. The internal surplus electricity can be sold to the large power grid connected to it, and electricity cannot be obtained from the large power grid. When the microgrid is in a grid-connected operation mode, the internal output entities of the microgrid can trade electricity within the microgrid, and can also buy and sell electricity between the microgrid and the large power grid.

[0066] Specifically, the first objective function is expressed as:

[0067]

[0068] Where F1 represents the first objective function; s i represents the i-th typical scenario; N s represents the number of typical scenes; ω(s i ) is a typical scenario i The probability of γ in-out is the electricity interaction cost coefficient; ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t They are respectively the amount of electricity sent from the large grid to the microgrid and the amount of electricity sent from the microgrid to the large grid during period t; Δt is the time interval.

[0069] It should be noted that the typical scenario i and the corresponding probability ω(s i ) is obtained based on the historical output data of wind power and photovoltaic power, which will be discussed in detail in step S04 of the present invention.

[0070] Step S02: Based on the rated power of each unit in the microgrid and the gas consumption of all gas turbines in the microgrid, a second objective function is constructed with the goal of minimizing the operating cost of the microgrid.

[0071] Specifically, the second objective function is expressed as:

[0072]

[0073] Where F2 represents the second objective function; θ is the microgrid component that needs to be maintained; Ω θ A collection of microgrid components that require maintenance, including wind turbines, photovoltaic units, gas turbines, and energy storage units; θ is the annual unit power operating cost of component θ; is the rated power of component θ; γ GT is the unit fuel cost; P CGT,t is the gas consumption of all gas turbines during period t; TL and γ RL are the unit demand response costs of the load that can be transferred and the load that can be reduced; ΔP TL,t and ΔP RL,t are the load transfer and load reduction in period t respectively.

[0074] It should be noted that the second objective function describes the self-management capability of the microgrid. Self-management capability means that the microgrid, as a controllable unit connected to a single grid point relative to the large grid, has a clear electrical boundary and realizes the internal integrated management of "source, grid, load and storage" through the deployment of an energy management system. Therefore, it is required that the microgrid can effectively manage its internal energy production, transmission, load and energy storage equipment through the energy management system to achieve autonomous operation and control.

[0075] Step S03: Based on the power generation of the gas turbines, the total power generation of the wind turbines, the total power generation of the photovoltaic units, the charging power and the discharging power of the energy storage units in the microgrid, a third objective function is constructed with the goal of maximizing the benefit of the microgrid.

[0076] Specifically, the third objective function is expressed as:

[0077]

[0078] Where F3 represents the third objective function; f c is the net income of gas turbine; f w is the net income of the wind turbine; f p all is the net income of the photovoltaic unit; f ES is the net income of the energy storage unit;

[0079] pc in (t) is the peak-valley time-of-use electricity price within the microgrid during period t; Q in c (t) is the electricity sold by the gas turbine in the microgrid during period t; E GT (t) is the power generation of the gas turbine during period t; (aE 2 GT (t)+bE GT (t)+c) is the amount of natural gas consumed by the gas turbine during period t; a, b and c are the natural gas consumption coefficients of the gas turbine respectively; P G (t) is the unit price of natural gas during period t;

[0080] Q in w (t) is the electricity sales of wind turbines in the microgrid during period t; pc b w Subsidize the price of wind power. is the total power generation of the wind turbine, where t=1,2,...24; pc w is the operating cost per unit of electricity generated by the wind turbine;

[0081] Q in p (t) is the electricity sales of the PV unit within the microgrid during period t; is the total power generation of the photovoltaic unit, where t=1,2,...24; pc b p is the photovoltaic electricity subsidy price; pc p is the operating cost per unit of electricity generated by the PV unit;

[0082] η ch is the charging efficiency of the energy storage device; η dis is the discharge efficiency of the energy storage device; P ch (t) is the charging power of the energy storage device; P dis (t) is the discharge power of the energy storage device; δ is a 0-1 variable, when δ is 1, it means that the energy storage is discharging, and when it is 0, it means that it is charging; ρ h is the peak electricity price; ρ l The off-peak electricity price; The amount of electricity sold by the provincial power grid from the self-generation and self-storage facilities of the new energy base; For backup power; It is the reserve compensation price per unit capacity.

[0083] Step S04: Based on the microgrid self-balancing constraint, power balance constraint, output constraint of each unit, power generation constraint and wind / solar uncertainty constraint, the first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan, including the microgrid operation mode in each time period, the interactive power between the microgrid and the large power grid, the gas turbine power generation, and the charging power and discharging power of the energy storage unit.

[0084] Specifically, the self-balancing capability of the microgrid refers to the ability of the microgrid to ensure the basic balance between local energy production and energy load by configuring an energy storage system when a high proportion of new energy is connected. In step S04, the self-balancing constraints of the microgrid include:

[0085] The annual interactive power between the microgrid and the large grid does not exceed the first preset proportion of the annual power consumption; the interactive power between the microgrid and the large grid includes the power sent from the large grid to the microgrid and the power sent from the microgrid to the large grid; the annual power consumption of the microgrid includes the annual power consumption of the microgrid users;

[0086] In the independent operation mode, the microgrid self-balancing constraint further includes:

[0087] The sum of the outputs of each unit in the microgrid satisfies the continuous power supply of the load in the microgrid for no less than two hours; the output of each unit includes wind power output, photovoltaic output, energy storage discharge power and gas turbine power generation power, and the load power supply in the microgrid includes the power load of microgrid users and energy storage charging power;

[0088] In the grid-connected operation mode, the microgrid self-balancing constraint further includes:

[0089] The microgrid has a demand-side response capability of no less than the second preset proportion of the maximum power load.

[0090] Furthermore, the first preset proportion is 50%, and the microgrid self-balancing constraint includes:

[0091] Where ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t The t period is the amount of electricity sent from the large grid to the microgrid, and the amount of electricity sent from the microgrid to the large grid; Δt is the time interval; T is the total number of time periods throughout the year, that is, the total duration of the year; P load,t is the total load power during period t, P CL,t is the normal load power, is the transferable load after the transfer in period t, is the load that can be reduced after reduction in period t;

[0092] It should be noted that, as mentioned above, when the microgrid is in an independent operation mode, the internal load demand is met through the cooperation of multiple output entities within the microgrid. The internal surplus electricity can be sold to the large power grid connected to it, but it cannot obtain electricity from the large power grid.

[0093] In the independent operation mode, the microgrid self-balancing constraints also include:

[0094]

[0095] Among them, P w (t), P p (t), P dis (t), P c (t), P ch (t) are wind power output, photovoltaic output, energy storage discharge power, gas turbine power generation power and energy storage charging power in period t; t0 is the initial period of the microgrid independent operation mode;

[0096] In the grid-connected operation mode, the second preset proportion is 10%, and the microgrid self-balancing constraint also includes:

[0097]

[0098] in, and They are respectively the upper limit of the amount that can be transferred and the upper limit of the amount that can be reduced during period t.

[0099] Furthermore, the transferable load needs to satisfy the following constraints:

[0100]

[0101] in, is the transferable load before the transfer in period t; ΔP TL,t is the transfer amount, which can be positive or negative; is the lower limit of the amount that can be transferred during period t.

[0102] Furthermore, the load that can be reduced needs to meet the following constraints:

[0103]

[0104] in, is the load that can be reduced before the reduction in period t; ΔP RL,t To reduce the amount, it can be positive or negative.

[0105] Specifically, in step S04, the power balance constraint is expressed as:

[0106]

[0107] in, are the power consumption rates of photovoltaic units, wind turbines, and single gas turbines, respectively. is the charge and discharge loss of energy storage, Q in (t) is the total amount of electricity sold within the microgrid during period t, Q out (t) is the total amount of electricity sold by the microgrid to the outside during period t.

[0108] Specifically, in step S04, the power generation constraint is expressed as:

[0109]

[0110] in, is the total user load in period t; K is the preset percentage value.

[0111] Specifically, in step S04, the energy storage unit constraint is expressed as:

[0112]

[0113] in, The final energy storage capacity of the previous day. They are the minimum powers of gas turbine, wind turbine, photovoltaic unit, energy storage discharge, and energy storage charging respectively; They are the maximum powers of gas turbine, wind turbine, photovoltaic unit, energy storage discharge, and energy storage charging respectively; is the internal electricity sales of the gas turbine during period t; is the internal electricity sales of wind power in period t; is the total amount of wind power sold during period t; is the internal photovoltaic power sales during period t; is the total photovoltaic electricity sold during period t.

[0114] Specifically, in step S04, the wind and solar uncertainty constraint is determined based on the following steps:

[0115] Based on the Gaussian kernel function, the probability density functions of wind power and photovoltaic output are constructed respectively;

[0116] The wind-solar combined output distribution function is obtained based on the probability density function of wind power and photovoltaic output through the Frank-Copula function;

[0117] The new energy scenario matrix is ​​obtained by random sampling based on the wind-solar combined output distribution function;

[0118] Based on the clustering of new energy scenario matrix, the set of typical new energy output scenarios and the probability of each typical output scenario are obtained;

[0119] Determine wind and solar uncertainty constraints based on typical output scenarios.

[0120] Furthermore, the probability density functions of wind power and photovoltaic output are constructed based on the Gaussian kernel function, including:

[0121] The historical output data of wind turbines and photovoltaic units in the microgrid are selected, with each hour as a point, and the Gaussian kernel function is used to generate the probability density function of wind turbine and photovoltaic output within 24 hours every day for n days, which is expressed as:

[0122]

[0123] Among them, X and Y are the output of wind turbine and photovoltaic respectively, X d , Y d are the wind power and photovoltaic power outputs on the dth day, respectively. h is the bandwidth parameter, which controls the width of the Gaussian kernel function, i.e., the degree of smoothness. K(·) is the Gaussian kernel function, i.e.:

[0124] Where exp represents the exponential function.

[0125] Furthermore, the wind-solar combined output distribution function is obtained based on the probability density function of wind power and photovoltaic output through the Frank-Copula function, which is expressed as:

[0126] F(X,Y)=C(F(X),F(Y));

[0127] Where C is a two-dimensional Frank-Copula function, that is:

[0128]

[0129] Among them, u=F(X), v=F(Y), λ is a related parameter, the value is between -1 and 1, and λ≠0.

[0130] Furthermore, the obtained wind-solar combined output distribution function is randomly sampled to obtain the new energy scenario matrix, which is expressed as:

[0131]

[0132] Where t is the number of sampling times, which can be 1, 2, ···M; w k Represents a certain renewable energy output, and its value is 1, 2, ···k, where k is the total number. The row vector represents each renewable energy output sequence, and the column vector is the result of each sampling. The column vector contains the correlation and complementarity information between different renewable energy sources (including wind power and photovoltaic power).

[0133] Furthermore, based on the clustering of the new energy scenario matrix, a set of typical new energy output scenarios and the probability of each typical output scenario are obtained, including:

[0134] Use the t-SNE algorithm to reduce the dimension of the renewable energy output scenario data;

[0135] The FCM clustering algorithm is used to reduce the dimension of the new energy output scene data to obtain the set of new energy typical output scenes and the probability of each typical output scene, which is expressed as:

[0136]

[0137] Among them, each S i Represents a scene, each scene contains wind power and photovoltaic output data; ω(s i ) is the probability of each scene; after clustering, there are N s The sum of the probabilities of all scenarios is 1.

[0138] Specifically, in step S04, the first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan including:

[0139] De-dimensionalize the values ​​of the first, second, and third objective functions;

[0140] Assign weights to each objective function to obtain a multi-objective model;

[0141] The multi-objective model is solved to obtain the optimal operation plan of the microgrid, including the microgrid operation mode in each period, the interactive power between the microgrid and the large grid, the gas consumption of the gas turbine, and the charging power and discharging power of the energy storage unit.

[0142] Furthermore, the values ​​of the first, second, and third objective functions are dimensionless using the membership function, which is expressed as:

[0143]

[0144] Among them, F i represents the function value of the i-th objective function; F i max and F i min They represent the maximum and minimum values ​​of the objective function respectively.

[0145] Furthermore, the subjective and objective integrated weighting method based on the analytic hierarchy process and entropy weight method assigns weights to each objective function. The specific calculation is as follows:

[0146]

[0147] Among them, w i represents the weight value obtained by the subjective and objective integrated weighting method; u i and v i They represent the weight values ​​calculated by the subjective analytic hierarchy process and the objective entropy weight method respectively; r represents the decision maker's preference coefficient for subjective and objective factors, r∈[0,1]. During implementation, the value is adjusted according to the operating requirements of the microgrid (for example, more emphasis on pursuing minimum cost or more emphasis on pursuing maximum benefit).

[0148] Furthermore, the joint objective function of the first, second and third objective functions is expressed as:

[0149] min f=w1π(F1)+w2π(F2)-w3π(F3).

[0150] Based on the microgrid self-balancing constraints, power balance constraints, energy storage unit constraints, power generation constraints, and wind and solar uncertainty constraints, the first, second, and third objective functions are jointly solved, and the optimal solution obtained includes the microgrid operation mode in each period, the interactive power between the microgrid and the large power grid, the gas turbine power generation, the charging power and discharge power of the energy storage unit, the wind power output, and the photovoltaic output. Furthermore, the microgrid optimization operation plan includes the microgrid operation mode in each period, the interactive power between the microgrid and the large power grid, the gas turbine power generation, and the charging power and discharge power of the energy storage unit.

[0151] The present embodiment discloses a method for optimizing the operation of a microgrid taking different operation modes into consideration. The method considers the independent operation mode and the grid-connected operation mode of the microgrid, the conventional load, the transferable load and the reducible load of the microgrid, constructs the self-balancing constraint of the microgrid, and takes the minimum operation cost and the maximum benefit as the goal by considering the uncertainty constraint of wind and solar power. The method solves and obtains the optimized operation plan of the microgrid, promotes the consumption of new energy, improves the energy utilization rate, and ensures the safe and stable operation of the microgrid and the large power grid. The method obtains a set of typical output scenarios of new energy based on historical data analysis, accurately describes the uncertainty output of new energy of the microgrid, thereby setting the uncertainty constraint of wind and solar power, and solving the objective function based on the uncertainty constraint of wind and solar power. The method can effectively deal with the uncertainty of new energy of the microgrid and ensure the stability of power operation in the microgrid. The method further improves the accuracy of the solution result by de-dimensionalizing the multi-objective function and assigning weights to each objective function based on the objective integrated weighting method of hierarchical analysis method and entropy weight method.

[0152] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A microgrid optimization operation method considering different operation modes, characterized in that: The steps include: Based on the microgrid operation mode and the interactive power between the microgrid and the large power grid, a first objective function is constructed with the goal of minimizing the interactive cost between the microgrid and the large power grid; the microgrid operation mode includes an independent operation mode and a grid-connected operation mode; Based on the rated power of each unit in the microgrid and the gas consumption of all gas turbines in the microgrid, the second objective function is constructed with the goal of minimizing the operating cost of the microgrid; Based on the power generation of gas turbines, the total power generation of wind turbines, the total power generation of photovoltaic units, the charging power and the discharging power of energy storage units in the microgrid, the third objective function is constructed with the goal of maximizing the benefits of the microgrid; Based on the microgrid self-balancing constraints, power balance constraints, energy storage unit constraints, power generation constraints and wind and solar uncertainty constraints, the first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan, including the microgrid operation mode in each period, the interactive power between the microgrid and the large power grid, the gas turbine power generation, and the charging power and discharging power of the energy storage unit.

2. The optimization operation method according to claim 1, characterized in that: The microgrid self-balancing constraints include: The annual interactive power between the microgrid and the large grid does not exceed the first preset proportion of the annual power consumption; the interactive power between the microgrid and the large grid includes the power sent from the large grid to the microgrid and the power sent from the microgrid to the large grid; the annual power consumption of the microgrid includes the annual power consumption of the microgrid users; In the independent operation mode, the microgrid self-balancing constraint further includes: The sum of the outputs of each unit in the microgrid satisfies the continuous power supply of the load in the microgrid for no less than two hours; the output of each unit includes wind power output, photovoltaic output, energy storage discharge power and gas turbine power generation power, and the load power supply in the microgrid includes the power load of microgrid users and energy storage charging power; In the grid-connected operation mode, the microgrid self-balancing constraint further includes: The microgrid has a demand-side response capability of no less than the second preset proportion of the maximum power load.

3. The optimization operation method according to claim 2, characterized in that: The first preset proportion is 50%, and the microgrid self-balancing constraint includes: Where ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t The t period is the amount of electricity sent from the large grid to the microgrid, and the amount of electricity sent from the microgrid to the large grid; Δt is the time interval; T is the total number of time periods throughout the year, that is, the total duration of the year; P load,t is the total load power during period t, P CL,t is the normal load power, is the transferable load after the transfer in period t, is the load that can be reduced after reduction in period t; In the independent operation mode, the microgrid self-balancing constraints also include: Among them, P w (t), P p (t), P dis (t), P c (t), P ch (t) are wind power output, photovoltaic output, energy storage discharge power, gas turbine power generation power and energy storage charging power in period t; t0 is the initial period of the microgrid independent operation mode; In the grid-connected operation mode, the second preset proportion is 10%, and the microgrid self-balancing constraint also includes: in, and They are respectively the upper limit of the amount that can be transferred and the upper limit of the amount that can be reduced during period t.

4. The optimization operation method according to any one of claims 1 to 3, characterized in that: Determine the wind and solar uncertainty constraints based on the following steps: Construct the probability density function of wind power and photovoltaic output based on Gaussian kernel function; The wind-solar combined output distribution function is obtained based on the probability density function of wind power and photovoltaic output through the Frank-Copula function; The new energy scenario matrix is ​​obtained by random sampling based on the wind-solar combined output distribution function; Based on the clustering of new energy scenario matrix, the set of typical new energy output scenarios and the probability of each typical output scenario are obtained; Determine wind and solar uncertainty constraints based on typical output scenarios.

5. The optimization operation method according to claim 4, characterized in that: Based on the microgrid operation mode and the interactive power between the microgrid and the large grid, the first objective function is constructed with the goal of minimizing the interactive cost between the microgrid and the large grid as expressed as: Where F1 represents the first objective function; s i represents the i-th typical scenario; N s represents the number of typical scenes; ω(s i ) is a typical scenario i The probability of γ in-out is the electricity interaction cost coefficient; ξ is a 0 / 1 variable representing the microgrid operation mode, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t The time period t is the amount of electricity sent from the large grid to the microgrid, and the amount of electricity sent from the microgrid to the large grid; Δt is the time interval.

6. The optimization operation method according to claim 5, characterized in that: The second objective function constructed based on the rated power of each unit in the microgrid and the gas consumption of all gas turbines in the microgrid with the goal of minimizing the operating cost of the microgrid is expressed as: Where F2 represents the second objective function; θ is the microgrid component that needs to be maintained; Ω θ A collection of microgrid components that require maintenance, including wind turbines, photovoltaic units, gas turbines, and energy storage units; θ is the annual unit operating cost of component θ; is the rated power of component θ; γ GT is the unit fuel cost; P CGT,t is the gas consumption of all gas turbines during period t; TL and γ RL are the unit demand response costs of the load that can be transferred and the load that can be reduced; ΔP TL,t and ΔP RL,t are the load transfer and load reduction in period t respectively.

7. The optimization operation method according to claim 6, characterized in that: The third objective function is constructed based on the gas consumption of the gas turbine in the microgrid, the total power generation of the wind turbine, the total power generation of the photovoltaic unit, the charging power and the discharging power of the energy storage unit, with the goal of maximizing the benefit of the microgrid, and is expressed as: Where F3 represents the third objective function; f c is the net income of gas turbine; f w is the net income of the wind turbine; f p all is the net income of the photovoltaic unit; f ES is the net income of the energy storage unit; pc in (t) is the peak-valley time-of-use electricity price within the microgrid during period t; Q in c (t) is the electricity sold by the gas turbine in the microgrid during period t; E GT (t) is the power generation of the gas turbine during period t; (aE 2 GT (t)+bE GT (t)+c) is the amount of natural gas consumed by the gas turbine during period t; a, b and c are the natural gas consumption coefficients of the gas turbine respectively; P G (t) is the unit price of natural gas during period t; Q in w (t) is the electricity sales of wind turbines in the microgrid during period t; pc b w Subsidize the price of wind power. is the total power generation of the wind turbine, where t=1,2,...24; pc w is the operating cost per unit of electricity generated by the wind turbine; Q in p (t) is the electricity sales of the PV unit within the microgrid during period t; is the total power generation of the photovoltaic unit, where t=1,2,...24; pc b p is the photovoltaic electricity subsidy price; pc p is the operating cost per unit of electricity generated by the PV unit; η ch is the charging efficiency of the energy storage device; η dis is the discharge efficiency of the energy storage device; P ch (t) is the charging power of the energy storage device; P dis (t) is the discharge power of the energy storage device; δ is a 0-1 variable, when δ is 1, it means that the energy storage is discharging, and when it is 0, it means that it is charging; ρ h is the peak electricity price; ρ l The off-peak electricity price; The amount of electricity sold by the self-generation and self-storage facilities of the new energy base from the provincial power grid; For backup power; It is the reserve compensation price per unit capacity.

8. The optimization operation method according to claim 7, characterized in that: The first, second and third objective functions are jointly solved to obtain the microgrid optimization operation plan, which includes: De-dimensionalize the values ​​of the first, second, and third objective functions; Assign weights to each objective function to obtain a multi-objective model; The multi-objective model is solved to obtain the optimal operation plan of the microgrid, including the microgrid operation mode in each period, the interactive power between the microgrid and the large grid, the gas consumption of the gas turbine, and the charging power and discharging power of the energy storage unit.

9. The optimization operation method according to claim 7, characterized in that: The power balance constraint is expressed as: in, They are the power consumption rates of photovoltaic units, wind turbines, and a single gas turbine respectively; is the charge and discharge loss of energy storage; Q in (t) is the total amount of electricity sold within the microgrid during period t, Q out (t) is the total amount of electricity sold by the microgrid to the outside during period t.

10. The optimization operation method according to claim 7, characterized in that: The power generation constraint is expressed as: in, is the total user load in period t; K is the preset percentage value.

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