A Microgrid Optimal Operation Method Considering Different Operation Modes

Abstract

The present invention relates to a method for optimizing the operation of a microgrid considering different operation modes, belonging to the technical field of microgrids. Based on the operation mode of the microgrid 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; based on the rated power of each unit in the microgrid and the gas consumption of the gas turbine, a second objective function is constructed with the goal of minimizing the operation cost; based on the power generation power of the gas turbine, the total power generation of the wind turbine group, the total power generation of the photovoltaic unit group, the charging power and discharging power of the energy storage unit, a third objective function is constructed with the goal of maximizing the microgrid revenue; based on the set constraints, multiple objective functions are jointly solved to obtain an optimized operation plan for the microgrid. The method of the present invention can effectively improve the utilization rate of new energy output in the microgrid, give full play to the regulation ability of flexible resources such as energy storage in the microgrid, and reduce the operation cost of the microgrid.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrids, and particularly relates to an optimal operation method for a microgrid considering different operation modes. Background Art

[0002] As an advanced technical means and power grid form, a microgrid is an effective way to promote the consumption of new energy, improve energy utilization efficiency, and reduce energy consumption costs. However, due to the obvious uncertainty and volatility of the output of distributed power sources within the microgrid, the currently operating and constructed microgrids lack a reasonable and standardized operation and management mode, unable to effectively cope with uncertainty and volatility, nor fully utilize the flexible regulation function of energy storage devices. In the case of large-scale access, problems such as reverse overload and curtailment of wind and light may occur, resulting in energy waste and posing challenges to the operation and control of the microgrid and the large power grid. Summary of the Invention

[0003] In view of the above analysis, the present invention aims to provide an optimal operation method for a microgrid considering different operation modes. By considering the independent operation mode and grid-connected operation mode of the microgrid, as well as the uncertainty constraints of wind and light in the microgrid, with the goal of minimizing operation costs and maximizing benefits, an optimal operation plan for the microgrid is obtained, promoting the consumption of new energy, improving energy utilization efficiency, and ensuring the safe and stable operation of the microgrid and the large power grid.

[0004] The method of the present invention includes 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, a second objective function is constructed with the goal of minimizing the operation 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, and the charging and discharging powers of energy storage units in the microgrid, a third objective function is constructed with the goal of maximizing the benefits of the microgrid;

[0008] Based on the self-balancing constraint, power balance constraint, energy storage unit constraint, power generation constraint, and wind-light uncertainty constraint of the microgrid, the first, second, and third objective functions are jointly solved to obtain an optimal operation plan for the microgrid, including the microgrid operation mode at each time period, the interactive power between the microgrid and the large power grid, the power generation of gas turbines, and the charging and discharging powers of energy storage units.

[0009] Further, the self-balancing constraint of the microgrid includes:

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

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

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

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

[0014] The microgrid has a demand - side response ability not less than the second preset proportion of the maximum power consumption load. Further, the first preset proportion is 50%; the self - balance constraint of the microgrid includes:

[0015] Among them, ξ is a 0 / 1 variable representing the operation mode of the microgrid, 0 represents the independent operation mode, and 1 represents the grid - connected operation mode; P in,t 、P out,t are the power sent from the main grid to the microgrid and the power sent from the microgrid to the main grid at time t respectively; Δt is the time interval; T is the total number of time periods in a year, that is, the total annual duration; P load,t is the total load power at time t, P CL,t is the conventional load power, is the transferable load after transfer at time t, is the reducible load after reduction at time t;

[0016] In the independent operation mode, the self - balance constraint of the microgrid further includes:

[0017]

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

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

[0020]

[0021] where and are the upper limit of the transferable amount and the upper limit of the reducible amount at time t, respectively.

[0022] Furthermore, the wind-solar uncertainty constraint is determined based on the following steps:

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

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

[0025] Randomly sample based on the wind-solar combined power output distribution function to obtain the new energy scenario matrix;

[0026] Cluster based on the new energy scenario matrix to obtain the new energy typical power output scenario set and the probabilities of each typical power output scenario;

[0027] Determine the wind-solar uncertainty constraint based on each typical power output scenario.

[0028] Furthermore, based on the microgrid operation mode and the interactive power between the microgrid and the large power grid, the first objective function is constructed with the goal of minimizing the interactive cost between the microgrid and the large power grid, which is 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 scenarios; ω(s i ) is the probability of the typical scenario s i ; γ in-out is the cost coefficient per unit of interactive power; 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 are the power sent from the large power grid to the microgrid and the power sent from the microgrid to the large power grid at time t, respectively; 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 microgrid operation cost, which is expressed as:

[0032]

[0033] Among them, F2 represents the second objective function; θ is the microgrid component to be maintained; Ω θ is the set of microgrid components to be maintained, including wind turbines, photovoltaic units, gas turbines, and energy storage units; β 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 at time t; γ TL and γ RL are the unit demand response costs of the shiftable load and the curtailable load respectively; ΔP TL,t and ΔP RL,t are the shiftable load quantity and the curtailable load quantity at time t respectively.

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

[0035]

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

[0037] pc in (t) is the time-of-use electricity price inside the microgrid at time t; Q in c (t) is the electricity sales volume of the gas turbine inside the microgrid at time t; E GT (t) is the power generation of the gas turbine at time t; (aE 2 GT (t)+bE GT (t)+c) is the natural gas consumption of the gas turbine at time 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 at time t;

[0038] Q in w (t) is the electricity sales volume of the wind turbine inside the microgrid at time t; pc b w is the wind power feed-in tariff, is the total power generation of the wind turbine, where n = 1, 2,... 24; pc w is the operating cost per unit power generation of the wind turbine;

[0039] Q in p (t) is the electricity sold by the photovoltaic unit within the microgrid during period t; is the total power generation of the photovoltaic unit, where n = 1, 2,... 24; pc b p is the feed-in tariff per unit of photovoltaic power; pc p is the operating cost per unit power generation of the photovoltaic unit;

[0040] η ch is the charging efficiency of the energy storage device; η dis is the discharging efficiency of the energy storage device; P ch (t) is the charging power of the energy storage device; P dis (t) is the discharging power of the energy storage device; δ is a 0-1 variable, when δ takes 1, it means the energy storage is discharging, and when it takes 0, it means it is charging; ρ h is the peak-time electricity price; ρ l is the valley-time electricity price; is the electricity sold to the provincial power grid by the self-generation and self-storage facilities of the new energy base; is the standby power; is the standby compensation price per unit capacity.

[0041] Further, the joint solution of the first, second, and third objective functions to obtain the optimal operation plan of the microgrid includes:

[0042] Carry out dimensionless processing on the values of the first, second, and third objective functions;

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

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

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

[0046]

[0047] Among them, are the plant electricity consumption rates of the photovoltaic unit, wind turbine, and single gas turbine respectively; is the charging and discharging loss of the energy storage; Q in (t) is the total 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 the t period.

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

[0049]

[0050] Wherein, is the total load of users during the t period; K is a preset percentage value.

[0051] The present invention can at least achieve 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 curtailable load of the microgrid, constructing the self-balancing constraint of the microgrid, and by considering the wind-solar uncertainty constraint, with the goal of minimizing the operation cost and maximizing the benefit, the optimal operation plan of the microgrid is solved, promoting the consumption of new energy, improving the energy utilization rate, and ensuring the safe and stable operation of the microgrid and the large power grid.

[0053] By obtaining the set of typical output scenarios of new energy based on historical data analysis, accurately characterizing the uncertain output of new energy in the microgrid, thus setting the wind-solar uncertainty constraint, and solving the objective function based on the wind-solar uncertainty constraint, it can effectively cope with the uncertainty of new energy in the microgrid and ensure the smoothness of power operation in the microgrid.

[0054] By dimensionless processing the multi-objective function and assigning weights to each objective function based on the objective integration weighting method of the analytic hierarchy process and entropy weight method, the accuracy of the solution result is further improved.

[0055] Other features and advantages of the present invention will be described in the subsequent specification, and some advantages can be made obvious from the specification, or understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained from the content specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0056] The drawings are only for the purpose of showing specific embodiments and are not considered as limiting the present invention. Throughout the drawings, the same reference signs represent the same components;

[0057] Figure 1 is the flowchart of the method of the present invention;

[0058] Figure 2 is the schematic diagram of the microgrid structure of the present invention. Detailed Embodiments

[0059] The preferred embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings. The accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0060] A specific embodiment of the present invention discloses a method for optimizing the operation of a microgrid considering different operation modes (as shown in Figure 1 the flowchart of the method of the present invention). A microgrid is a small-scale power system, including distributed renewable units (wind turbines and photovoltaic units), micro gas turbines, electrical energy storage, and loads. The microgrid is also connected to the public grid, i.e., the large grid, by a line. As shown in Figure 2 the schematic diagram of the microgrid structure.

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

[0062] Furthermore, in the present invention, to maximize the promotion of clean energy consumption, it is assumed that the gas turbine and the energy storage must cooperate with the wind and light clean energy units and cannot operate independently, and jointly meet the electrical load requirements of the internal users in the microgrid; to ensure the stability of energy use and the safety of the power grid, it is assumed that the wind and light clean energy units cannot operate independently and must cooperate with other stable generating units to jointly meet the electrical load requirements within the microgrid.

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

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

[0065] Specifically, when the microgrid is in the independent operation mode, it preferentially meets the internal load demand through the cooperation of multiple output entities within the microgrid. The internal surplus power can be sold to the large grid connected to it, and power cannot be obtained from the large grid; when the microgrid is in the grid-connected operation mode, the output entities within the microgrid can conduct power trading within the microgrid or conduct power trading with the large grid through the microgrid.

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

[0067]

[0068] Among them, F1 represents the first objective function; s i represents the i-th typical scenario; N s represents the number of typical scenarios; ω(s i ) is the probability of the typical scenario s i ; γ in-out is the cost coefficient of electricity interaction per unit; ξ is a 0 / 1 variable representing the operation mode of the microgrid, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t are respectively the electricity quantity sent from the large power grid to the microgrid and the electricity quantity sent from the microgrid to the large power grid at time t; Δt is the time interval.

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

[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, construct a second objective function with the goal of minimizing the operation cost of the microgrid.

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

[0072]

[0073] Among them, F2 represents the second objective function; θ is the microgrid component to be maintained; Ω θ is the set of microgrid components to be maintained, including wind turbines, photovoltaic units, gas turbines, and energy storage units; β θ is the annual unit power operation 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 at time t; γ TL and γ RL are respectively the unit demand response costs of shiftable load and curtailable load; ΔP TL,t and ΔP RL,t are respectively the shiftable load quantity and curtailable load quantity at time t.

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

[0075] Step S03: Based on the power generation of the gas turbine, the total power generation of the wind turbine group, the total power generation of the photovoltaic unit group, the charging power and discharging power of the energy storage unit in the micro - grid, construct a third objective function with the maximization of the micro - grid revenue as the goal.

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

[0077]

[0078] Among them, F3 represents the third objective function; f c is the net revenue of the gas turbine; f w is the net revenue of the wind turbine group; f p is the net revenue of the photovoltaic unit group; f ES is the net revenue of the energy storage unit group;

[0079] pc in (t) is the time - of - use electricity price inside the micro - grid at time t; Q in c (t) is the electricity sales volume of the gas turbine inside the micro - grid at time t; E GT (t) is the power generation of the gas turbine at time t; (aE 2 GT (t)+bE GT (t)+c) is the natural gas consumption of the gas turbine at time 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 at time t;

[0080] Q in w (t) is the electricity sales volume of the wind turbine group inside the micro - grid at time t; pc b w is the electricity subsidy price for wind power, is the total power generation of the wind turbine group, where n = 1, 2,... 24; pc w is the operating cost per unit power generation of the wind turbine group;

[0081] Q in p (t) is the electricity sales volume of the photovoltaic unit group inside the micro - grid at time t; is the total power generation of the photovoltaic unit, where n = 1, 2,... 24; pc b p is the photovoltaic subsidy price per kilowatt-hour; pc p is the operating cost per unit power generation of the photovoltaic unit;

[0082] η ch is the charging efficiency of the energy storage device; η dis is the discharging efficiency of the energy storage device; P ch (t) is the charging power of the energy storage device; P dis (t) is the discharging power of the energy storage device; δ is a 0-1 variable. When δ takes 1, it means the energy storage is discharging, and when it takes 0, it means it is charging; ρ h is the peak-time electricity price; ρ l is the valley-time electricity price; is the electricity quantity sold to the provincial power grid by the self-generation and self-storage facilities of the new energy base; is the reserve electricity; is the reserve compensation price per unit capacity.

[0083] Step S04: Jointly solve the first, second, and third objective functions based on the microgrid self-balancing constraint, power balance constraint, output constraints of each unit, power generation constraint, and wind-solar uncertainty constraint to obtain the optimal operation plan of the microgrid, including the operation mode of the microgrid at each time period, the interactive electricity quantity between the microgrid and the large power grid, the power generation power of the gas turbine, and the charging and discharging powers of the energy storage unit.

[0084] Specifically, the self-balancing ability of the microgrid refers to the ability of the microgrid to ensure the basic balance of local energy production and energy consumption load by configuring an energy storage system under the condition of high proportion of new energy access. In step S04, the microgrid self-balancing constraint includes:

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

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

[0087] The sum of the outputs of each unit of the microgrid satisfies that the load in the microgrid is continuously powered for no less than two hours; the outputs of each unit include wind power output, photovoltaic output, energy storage discharging power, and gas turbine power generation power, and the load power supply in the microgrid includes the electricity consumption load of microgrid users and the 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 capacity not less than the second preset proportion of the maximum electricity load.

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

[0091] Among them, ξ is a 0 / 1 variable representing the operation mode of the microgrid, 0 represents the independent operation mode, and 1 represents the grid-connected operation mode; P in,t , P out,t are the electricity sent from the large power grid to the microgrid and the electricity sent from the microgrid to the large power grid at time t respectively; Δt is the time interval; T is the total number of time periods in a year, i.e., the total annual duration; P load,t is the total load power at time t, P CL,t is the conventional load power, is the shiftable load after transfer at time t, is the curtailable load after curtailment at time t;

[0092] It should be noted that, as mentioned above, when the microgrid is in the independent operation mode, it preferentially meets the internal load demand through the cooperation of multiple power generation 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;

[0093] In the independent operation mode, the self-balancing constraint of the microgrid further includes:

[0094]

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

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

[0097]

[0098] Among them, and are the upper limits of the shiftable amount and the curtailable amount at time t respectively.

[0099] Furthermore, the shiftable load needs to meet the following constraints:

[0100]

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

[0102] Furthermore, the load that can be curtailed needs to satisfy the following constraints:

[0103]

[0104] Among them, is the load that can be curtailed before curtailment in the t period; ΔP RL,t is the curtailment amount, which can be positive or negative.

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

[0106]

[0107] Among them, are the auxiliary power rates of the photovoltaic unit, wind turbine unit, and single gas turbine respectively, is the charge and discharge loss of the energy storage, Q in (t) is the total internal power sales volume of the microgrid in the t period, Q out (t) is the total external power sales volume of the microgrid in the t period.

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

[0109]

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

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

[0112]

[0113] Among them, is the final energy storage power of the previous day, are the minimum powers of the gas turbine, wind turbine unit, photovoltaic unit, energy storage discharge, and energy storage charge respectively; are the maximum powers of the gas turbine, wind turbine unit, photovoltaic unit, energy storage discharge, and energy storage charge respectively; is the internal power sales volume of the gas turbine in the t period; is the internal power sales volume of the wind power in the t period; is the total external power sales volume of the wind power in the t period; is the internal power sales volume of the photovoltaic system during period t; is the total external power sales volume of the photovoltaic system during period t.

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

[0115] Construct the probability density functions of wind power and photovoltaic power output respectively based on the Gaussian kernel function;

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

[0117] Randomly sample based on the wind-solar combined power output distribution function to obtain the new energy scenario matrix;

[0118] Cluster based on the new energy scenario matrix to obtain the set of typical new energy power output scenarios and the probabilities of each typical power output scenario;

[0119] Determine the wind-solar uncertainty constraint based on each typical power output scenario.

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

[0121] Select the historical power output data of the wind turbines and photovoltaic units in the microgrid. Each hour is a point. Use the Gaussian kernel function to generate the probability density functions of the wind turbine and photovoltaic power output within 24 hours of each day for n days, expressed as:

[0122]

[0123] where X and Y are the power outputs of the wind turbine and photovoltaic respectively, X d , Y d are the power outputs of the wind power and photovoltaic on the d-th day respectively, h is the bandwidth parameter, which controls the width of the Gaussian kernel function, that is, the smoothness, and K(·) is the Gaussian kernel function, that is:

[0124] where exp represents the exponential function.

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

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

[0127] where C is the two-dimensional Frank-Copula function, that is:

[0128]

[0129] where \(u = F(X)\), \(v = F(Y)\), \(\lambda\) is a correlation parameter with a value between -1 and 1, and \(\lambda\neq0\).

[0130] Further, random sampling is performed on the obtained combined wind-solar power output distribution function to obtain a new energy scenario matrix, expressed as:

[0131]

[0132] where \(t\) is the number of sampling times, with a value of \(1, 2, \cdots, M\); \(w\) k represents a certain new energy output, with a value of \(1, 2, \cdots, k\), where \(k\) is the total number. The row vector represents each new energy output sequence, and the column vector is the result of each sampling. The column vector contains the correlation, that is, complementary information, between different new energies (including wind power and photovoltaic power).

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

[0134] Using the t-SNE algorithm to perform dimensionality reduction on the new energy output scenario data;

[0135] Using the FCM clustering algorithm for the dimensionality-reduced new energy output scenario data to obtain a set of typical new energy output scenarios and the probabilities of each typical output scenario, expressed as:

[0136]

[0137] where each \(S\) i represents a scenario, and each scenario contains the output data of wind power and photovoltaic power; \(\omega(s\) i ) is the probability of each scenario; after clustering, there are a total of \(N\) s scenarios, and 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 optimal operation plan, including:

[0139] Performing dimensionless processing on the numerical values of the first, second, and third objective functions;

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

[0141] Solving the multi-objective model to obtain the microgrid optimal operation plan, including the microgrid operation mode at each time period, the interaction power between the microgrid and the large power grid, the gas consumption of the gas turbine, the charging power and discharging power of the energy storage unit.

[0142] Further, using the membership function to perform dimensionless processing on the numerical values of the first, second, and third objective functions, and the membership function 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 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 the entropy weight method assigns weights to each objective function, and 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 represent the weight values calculated by the subjective analytic hierarchy process and the objective entropy weight method respectively; r represents the preference coefficient of the decision maker for subjective and objective factors, r ∈ [0, 1]. During implementation, the value is adjusted according to the operation requirements of the microgrid (exemplarily, more emphasis on pursuing the minimum cost or more emphasis on pursuing the maximum benefit).

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

[0149] minf = w1π(F1) + w2π(F2) - w3π(F3).

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

[0151] A microgrid optimal operation method considering different operation modes disclosed in this embodiment, by considering the independent operation mode and grid-connected operation mode of the microgrid, considering the conventional load, shiftable load, and curtailable load of the microgrid, constructing the self-balancing constraint of the microgrid, and by considering the uncertainty constraint of wind and light, with the goal of minimizing operation cost and maximizing revenue, solving to obtain the microgrid optimal operation plan, promoting new energy consumption, improving energy utilization efficiency, and ensuring the safe and stable operation of the microgrid and the large power grid; by obtaining the set of typical output scenarios of new energy based on historical data analysis, accurately characterizing the uncertain output of new energy in the microgrid, thereby setting the uncertainty constraint of wind and light, and solving the objective function based on the uncertainty constraint of wind and light, it can effectively cope with the uncertainty of new energy in the microgrid and ensure the smoothness of power operation in the microgrid; by dimensionless processing the multi-objective function and assigning weights to each objective function based on the objective integration weighting method of the analytic hierarchy process and entropy weight method, further improving the accuracy of the solution results.

[0152] As mentioned above, it is only the 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 thought of by those skilled in the art 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 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; the first objective function is 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 power grid to the microgrid, and the amount of electricity sent from the microgrid to the large power 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; 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 time period, the interactive power between the microgrid and the large power grid, the gas turbine power generation power, and the charging power and discharging power of the energy storage unit; wherein, in the independent operation mode, the microgrid self-balancing constraints include: the sum of the outputs of each unit in the microgrid meets the continuous power supply of the load in the microgrid for not 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 the energy storage charging power; in the grid-connected operation mode, the microgrid self-balancing constraints include: the microgrid has a demand-side response capability that is not less than the second preset proportion of the maximum power load.

2. The optimization operation method according to claim 1, characterized in that: The microgrid self-balancing constraint also includes: The annual interactive electricity volume between the microgrid and the large grid does not exceed a first preset proportion of the annual electricity consumption; the interactive electricity volume between the microgrid and the large grid includes the electricity volume sent from the large grid to the microgrid and the electricity volume sent from the microgrid to the large grid; the annual electricity consumption of the microgrid includes the annual electricity consumption of the microgrid users.

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: 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.

6. The optimization operation method according to claim 5, 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 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 n = 1, 2, ... 24; pc w is the operating cost of the wind turbine per unit of power generation; 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 n = 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 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.

7. The optimization operation method according to claim 6, 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.

8. The optimization operation method according to claim 6, 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.

9. The optimization operation method according to claim 6, 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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