Microgrid operation planning method and system based on improved Archimedes optimization algorithm

Abstract

The invention relates to a micro-grid operation planning method and system based on an improved Archimedes optimization algorithm, and the method comprises the steps: obtaining the operation data of a micro-grid control system, and calculating the operation cost of the micro-grid control system based on the operation data; a micro-grid operation planning optimization model is constructed, a corresponding objective function is constructed with operation cost minimization as an objective, constraint conditions are set based on operation data, and the constraint conditions comprise a distributed power supply output power constraint, a power load and power balance equality constraint, a unit ramp rate constraint and a power interaction constraint; solving the micro-grid operation planning optimization model by using an improved Archimedes optimization algorithm based on reverse learning and a multi-direction strategy to obtain a micro-grid operation planning scheme; the power loss and consumption cost are reduced to the maximum extent, and the stability of the whole power system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of microgrid operation planning, and mainly to a microgrid operation planning method and system based on an improved Archimedean optimization algorithm. Background Art

[0002] The operation planning of Micro-Grid Control System (MCS) is one of the important policy tasks to consider in increasing the share of clean energy generation, providing a safe and symmetrical power grid in decentralized natural power sources, minimizing power losses and consumer costs, and ensuring the stability of the entire power system. Microgrid is a new power supply solution at the end of the distribution network that can effectively receive intermittent and distributed power generation output while improving the reliability of power supply to consumers.

[0003] As global energy demand continues to grow, grid reliability and power quality pose greater challenges to distribution operation planning. Frequent extreme weather events, aging and insufficient capacity of grid infrastructure, widespread access to distributed energy, and increased cybersecurity threats all pose severe challenges to the stable operation of the grid. Traditional planning methods are difficult and computationally expensive for large-scale nonlinear problems, such as linear programming, least squares equations, and other typical operation planning.

[0004] For example, a Chinese invention patent with publication number "CN105894108A" discloses a "Microgrid Operation Planning Method and System", which specifically discloses "obtaining parameter information of distributed units and energy storage systems in a microgrid, taking the lowest total cost of microgrid operation within the planning period as the objective function, and constructing a microgrid planning model containing an energy storage system based on the parameter information; obtaining pre-constructed model constraints, and using a harmonic search algorithm to solve the planning model based on the model constraints; obtaining the optimal solution of the planning model, and deriving a corresponding microgrid operation planning scheme based on the optimal solution", but the performance of the harmonic search algorithm used in this method depends on the construction of an initial harmonic memory library. If the initial solution is not selected properly, the algorithm will fall into a local optimal solution during the search process and will not be able to find a global optimal solution; in addition, for large-scale microgrid planning problems, the harmonic search algorithm may require a long computing time and high computing resources. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present application provides a microgrid operation planning method and system based on an improved Archimedes optimization algorithm.

[0006] The technical solution of this application is as follows:

[0007] On the one hand, the present invention proposes a microgrid operation planning method based on an improved Archimedean optimization algorithm, the method comprising:

[0008] Acquiring operation data of a microgrid control system, the microgrid control system including a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the operation data including micro turbine data, wind turbine data, diesel engine data, photovoltaic power source data, fuel cell data, battery data and related microgrid data; calculating the operation cost of the microgrid control system based on the operation data;

[0009] Constructing a microgrid operation planning optimization model, wherein the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and constraints set based on the operation data, wherein the constraints include distributed power output constraints, power load and power balance equality constraints, unit ramp rate limits, and power interaction constraints;

[0010] The improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy is used to solve the microgrid operation planning optimization model and obtain the microgrid operation planning scheme.

[0011] Preferably, the method further comprises performing data cleaning on the operating data, wherein the data cleaning comprises processing missing values, outliers and standardizing the data format.

[0012] Preferably, in the operation data:

[0013] The micro-turbine data includes an output power of the micro-turbine, an upper limit of the output power of the micro-turbine, and an efficiency of the micro-turbine;

[0014] The wind turbine data includes an output power of the wind turbine, an upper limit of the output power of the wind turbine, an efficiency of the wind turbine, a rated power of the wind turbine, a speed of the wind turbine, a wind shear speed, a rated wind speed, a wind shear speed and a parameter related to a characteristic curve of the output power of the wind turbine;

[0015] The diesel engine data includes the output power of the diesel engine, the upper limit of the output power of the diesel engine and the fuel cost coefficient of the diesel engine;

[0016] The photovoltaic power source data includes the output power of the photovoltaic power source, the upper limit of the output power of the photovoltaic power source, the rated power value of the photovoltaic power source, the output efficiency of the photovoltaic power source, the rated power of the fan, the actual light illumination of the sampling point, the temperature coefficient of the sampling point, the photovoltaic environment temperature value and the temperature value;

[0017] The fuel cell data includes the output power of the fuel cell, the upper limit of the output power of the fuel cell and the output efficiency of the fuel cell;

[0018] The battery data includes the output power of the battery, the upper limit of the output power of the battery, the charging power, the discharging power, the variable rate of charging and discharging, the charging efficiency and the discharging efficiency;

[0019] The relevant microgrid data include natural gas price, lower calorific value of natural gas, power output of distributed power sources, minimum active power output of distributed power sources, maximum active power output of distributed power sources, power load, power of the common coupling point of the exchange grid between the microgrid and the large grid, minimum power of the common coupling point of the exchange grid and maximum power of the common coupling point of the exchange grid.

[0020] Preferably, the operating cost of the microgrid control system includes:

[0021] The operating cost of a microturbine is expressed as:

[0022]

[0023]

[0024] In the formula, C MT (t) represents the operating cost of the microturbine at time t; G price represents the natural gas price; L gas Indicates the lower calorific value of natural gas; P MT (t) represents the output power of the microturbine at time t; η MT (t) represents the efficiency of the microturbine at time t; t represents time;

[0025] The operating cost of a wind turbine is expressed as:

[0026]

[0027] In the formula, C WT (t) represents the operating cost of the wind turbine at time t; P WT (t) represents the output power of the wind turbine at time t; η WT (t) represents the efficiency of the wind turbine at time t; P v (t) represents the rated power of the fan at time t; v(t) represents the speed of the fan at time t; v ci Indicates wind shear velocity; v r Indicates rated wind speed; v co represents the wind shear speed; A, B and C represent parameters related to the characteristic curve of the wind turbine output power;

[0028] The operating cost of a diesel engine is expressed as:

[0029] CDE (t) = a(P DE (t)) 2 +bP DE (t)+c;

[0030] In the formula, C DE (t) represents the operating cost of the diesel engine at time t; P DE (t) represents the output power of the diesel engine at time t; a, b and c represent the fuel cost coefficients of the diesel engine;

[0031] The operating cost of photovoltaic power source is expressed as:

[0032]

[0033] In the formula, C PV (t) represents the operating cost of the photovoltaic power source at time t; P PV (t) represents the output power of the photovoltaic power source at time t; P rPV (t) represents the rated power value supplied to the photovoltaic power source at time t; η PV Represents the output efficiency of photovoltaic power source; P v (t) represents the rated power of the fan at time t; I F (t) represents the actual light intensity at sampling point t; t represents the temperature coefficient at sampling point t; T(t) represents the photovoltaic environment temperature at time t; T st (t) represents the temperature value tested under the same environment at time t;

[0034] The operating cost of a fuel cell is expressed as:

[0035]

[0036] η FC (t) = 0.6735 - 0.0023 × P FC (t);

[0037] In the formula, C FC (t) represents the operating cost of the fuel cell at time t; P FC (t) represents the output power of the fuel cell at time t; η FC (t) represents the output efficiency of the fuel cell at time t; Δt represents the time period;

[0038] The operating cost of the battery is expressed as:

[0039]

[0040] In the formula, C SB (t) represents the operating cost of the battery at time t; PSB (t) represents the output power of the battery at time t; P ESB,ch (t) represents the charging power at time t; P ESB,dis (t) represents the discharge power at time t; τ represents the variable rate coefficient of battery charge and discharge; represents the charging efficiency at time t; Represents the discharge efficiency at time t.

[0041] Preferably, the corresponding objective function is constructed with the goal of minimizing the operating cost, which can be expressed as follows:

[0042] minF=C MT +C WT +C DE +C PV +C FC +C SB ;

[0043] Where F represents the objective function; C MT represents the operating cost of the microturbine; C WT represents the operating cost of the wind turbine; C DE Represents the operating cost of a diesel engine; C PV Represents the operating cost of photovoltaic power source; C FC represents the operating cost of the fuel cell; C SB Indicates the operating cost of the battery.

[0044] Preferably, the output power constraint of the distributed power source is expressed as:

[0045] P j,min ≤P j (t)≤P j,max ;

[0046] Where P j (t) represents the power output of the jth power supply at time t; P j,min represents the minimum active power output of the jth power supply; P j,max represents the maximum active power output of the jth power supply;

[0047] The power load and the output power constraint of the power balance are expressed as:

[0048] P load (t) = P MT +P WT +P DE +P PV +P FC +P SB +u·P EX ;

[0049] Where P load (t) represents the power load at time t; P EX Indicates the power of the common coupling point between the microgrid and the large grid; u indicates whether there is power exchange between the microgrid and the large grid. If so, the value is 1, otherwise, the value is 0;

[0050] The unit ramp rate limit is expressed as follows:

[0051]

[0052] In the formula, represents the upper limit of the output power of the microturbine; represents the upper limit of the wind turbine's output power; Indicates the upper limit of diesel engine output power; Indicates the upper limit of the output power of the photovoltaic power source; Indicates the upper limit of the output power of the fuel cell; Indicates the upper limit of the battery's output power;

[0053] The power interaction constraint is expressed as follows:

[0054] P EX,min ≤P EX (t)≤P EX,max ;

[0055] Where P EX (t) represents the power of the common coupling point between the microgrid and the large grid at time t; P EX,min Represents the minimum power of the common coupling point of the switching grid; P EX,max Indicates the maximum power at the point of common coupling of the switching grid.

[0056] Preferably, in the step of solving the microgrid operation planning optimization model by using an improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to obtain a microgrid operation planning scheme:

[0057] The improved Archimedean optimization algorithm includes an initialization phase, an attribute update phase, a conversion variable calculation phase, an exploration phase, a development phase, and an evaluation phase, specifically:

[0058] The initialization stage specifically initializes the candidate operation plan schemes, the range of the candidate operation plan schemes, the density of the candidate operation plan schemes, and the moving speed of the candidate operation plan schemes, which can be expressed as:

[0059] x i =lb i +rand1()×(ub i -lb i), i=1,2,...,N;

[0060] vol i =rand1();

[0061] den i =rand1();

[0062] acc i =lb i +rand1()×(ub i -lb i );

[0063] In the formula, x i represents the i-th candidate operation plan; N represents the number of candidate operation plan plans; rand1() represents a d-dimensional vector randomly generated in the interval [0, 1]; d represents the preset dimension; lb i represents the lower limit of the value of the candidate solution of the i-th operation plan; ub i represents the upper limit of the value of the i-th operation plan candidate; i represents the index value of the i-th operation plan candidate; vol i represents the range of candidate solutions for the i-th operation plan; i represents the density of the candidate plan for the i-th operation plan; acc i represents the moving speed of the i-th operation planning candidate;

[0064] The updating attribute stage specifically calculates the objective function values ​​of all the operation planning candidate solutions, selects the operation planning candidate solution with the smallest objective function value as the current optimal operation planning solution, and updates the range and density of the operation planning candidate solutions, which can be expressed as follows:

[0065]

[0066] In the formula, represents the range of candidate solutions for the ith operation plan in the nth iteration; vol best represents the range of the best candidate solutions; Indicates the density of the candidate solutions for the ith operation plan in the nth iteration; vol best Indicates the density of the best candidate solution; n indicates the index value of the nth iteration;

[0067] The calculation conversion variable stage is expressed as follows:

[0068]

[0069] Where TF represents the transformation variable; n represents the index value of the nth iteration; n maxrepresents the maximum number of iterations; exp() represents the exponential function; d represents the density factor;

[0070] The exploration phase is specifically when TF≤0.5, there is a collision between the running planning candidate solutions; when TF>0.5, there is no collision between the running planning candidate solutions, and the moving speed of the running planning candidate solutions is updated, which is expressed as:

[0071]

[0072] In the formula, represents the moving speed of the candidate solution of the ith operation plan in the nth iteration; mr Indicates the density of random planning candidate solutions; vol mr Indicates the range of random materials; acc mr represents the moving speed of the random planning candidate solution; mr represents the index value of the random planning candidate solution;

[0073] The moving speed of the i-th operation plan candidate solution of the n-th iteration is normalized and expressed as:

[0074]

[0075] In the formula, represents the normalized moving speed of the ith operation plan candidate of the nth iteration; μ represents the preset normalization range; λ represents the preset normalization range; max() represents the maximum value function; min() represents the minimum value function;

[0076] The development stage specifically uses the reverse learning strategy to update the operation planning candidate solution and the multi-directional strategy to run the plan to update the moving direction of the operation planning candidate solution, wherein:

[0077] The reverse learning strategy specifically calculates and evaluates the forward and reverse solutions of the operation plan candidate schemes at the same time, calculates the objective function values ​​of the forward and reverse solutions, and sorts them, and selects the operation plan candidate schemes whose objective function values ​​are within a preset range as the new operation plan candidate set, which is expressed in the formula:

[0078]

[0079] S new =S×α r ;

[0080] x i '=ub i +lb i -x i ;

[0081] S={x'1,x'2,...,x' N};

[0082] In the formula, S new represents the new operation plan candidate set; S represents the reverse solution set; x' i represents the i-th reverse solution; α r represents the adjustment coefficient of the new operation plan candidate set; L istar represents the distance between the preset ideal operation plan and the second best candidate plan; rand2() represents a random function; β represents the preset lower bound of the random function; γ represents the preset upper bound of the random function;

[0083] Update the candidate operation plan, expressed as:

[0084]

[0085] T = TF * K3;

[0086] In the formula, represents the optimal operation plan at the nth iteration; K1 represents a preset constant; K2 represents a preset constant; R represents a random number in the interval [0, 1]; x R represents the Rth operation plan candidate; T represents the proportional variable; F new It represents the guiding factor that changes the moving direction of the operation planning candidate solution;

[0087] Among them, the moving direction of the candidate scheme of the operation planning is updated by using a multi-directional strategy, specifically adding random numbers to move the guidance factor in multiple directions, which can be expressed as:

[0088]

[0089] W = 2R-K4;

[0090] Where, K4 represents the preset constant; W represents the search range;

[0091] The evaluation phase specifically calculates the corresponding objective function value of the operation planning candidate scheme after each iterative update, and updates the current optimal operation planning scheme; until the maximum number of iterations is reached or the objective function value converges, the operation planning scheme is obtained.

[0092] On the other hand, the present invention also proposes a microgrid operation planning system based on an improved Archimedean optimization algorithm, the system comprising a data acquisition module, an operation planning optimization model, an optimization solution module and a solution output module, wherein:

[0093] The data acquisition module is used to acquire the operation data of the microgrid control system, the microgrid control system includes a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the operation data includes micro turbine data, wind turbine data, diesel engine data, photovoltaic power source data, fuel cell data, battery data and related microgrid data; based on the operation data, the operation cost of the microgrid control system is calculated; the operation data and the operation cost are transmitted to the operation planning optimization model construction module;

[0094] The operation planning optimization model construction module is used to construct a microgrid operation planning optimization model, and the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and constraints set based on the operation data, and the constraints include distributed power output power constraints, power load and power balance equality constraints, unit climbing rate limits and power interaction constraints;

[0095] The optimization solution module uses an improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to solve the microgrid operation planning optimization model to obtain a microgrid operation planning scheme; the microgrid operation planning scheme includes optimal planning parameters and optimal operation costs;

[0096] The scheme output module is used to display the microgrid operation planning scheme.

[0097] On the other hand, the present invention further proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any embodiment of the present invention is implemented.

[0098] On the other hand, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any embodiment of the present invention.

[0099] Compared with the prior art, the present invention has the following beneficial effects:

[0100] 1) The present invention provides a microgrid operation planning method and system based on an improved Archimedean optimization algorithm, which obtains detailed operation data of each component in a microgrid control system and calculates the operation cost of each component, thereby not only improving the accuracy of cost calculation, but also enhancing the utilization efficiency of operation data in a microgrid control system;

[0101] 2) The present invention provides a microgrid operation planning method and system based on an improved Archimedean optimization algorithm, and a microgrid operation planning optimization model constructed based on operation cost data, with the goal of minimizing operation cost, while taking into account the actual operation conditions of power supply output power constraint, power load and power balance equality constraint, unit ramp rate limit and power interaction constraint, which not only enhances the scientific nature of the planning scheme, but also improves the economy of microgrid operation and ensures that the power demand is met;

[0102] 3) The present invention provides a microgrid operation planning method and system based on an improved Archimedean optimization algorithm, which utilizes an improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to solve the microgrid operation planning optimization model, and can efficiently search for the global optimal solution or approximate optimal solution, thereby improving the solution efficiency, improving the quality of the planning scheme, and providing more reliable and optimized guidance for the actual operation of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 is a method flow chart of an embodiment of the present invention;

[0104] Figure 2 Schematic diagram of a microgrid control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0105] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0106] The present invention provides the following technical solution: a microgrid operation planning method and system based on an improved Archimedean optimization algorithm.

[0107] Example 1

[0108] See Figure 1 This embodiment provides a microgrid operation planning method based on an improved Archimedean optimization algorithm, and the specific steps include:

[0109] S1. Obtain the operating data of the microgrid control system. Please refer to Figure 2 , the microgrid control system includes a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the voltage control point is connected to the microgrid using a point common coupling;

[0110] The method further includes performing data cleaning on the operation data, wherein the data cleaning includes processing missing values, abnormal values ​​and unifying data formats;

[0111] S11, the operation data includes micro turbine data, wind turbine data, diesel engine data, photovoltaic power supply data, fuel cell data, battery data and related microgrid data;

[0112] The micro-turbine data includes an output power of the micro-turbine, an upper limit of the output power of the micro-turbine, and an efficiency of the micro-turbine;

[0113] The wind turbine data includes an output power of the wind turbine, an upper limit of the output power of the wind turbine, an efficiency of the wind turbine, a rated power of the wind turbine, a speed of the wind turbine, a wind shear speed, a rated wind speed, a wind shear speed and a parameter related to a characteristic curve of the output power of the wind turbine;

[0114] The diesel engine data includes the output power of the diesel engine, the upper limit of the output power of the diesel engine and the fuel cost coefficient of the diesel engine;

[0115] The photovoltaic power source data includes the output power of the photovoltaic power source, the upper limit of the output power of the photovoltaic power source, the rated power value of the photovoltaic power source, the output efficiency of the photovoltaic power source, the rated power of the fan, the actual light illumination of the sampling point, the temperature coefficient of the sampling point, the photovoltaic environment temperature value and the temperature value;

[0116] The fuel cell data includes the output power of the fuel cell, the upper limit of the output power of the fuel cell and the output efficiency of the fuel cell;

[0117] The battery data includes the output power of the battery, the upper limit of the output power of the battery, the charging power, the discharging power, the variable rate of charging and discharging, the charging efficiency and the discharging efficiency;

[0118] The relevant microgrid data include natural gas price, lower calorific value of natural gas, power output of distributed power sources, minimum active power output of distributed power sources, maximum active power output of distributed power sources, power load, power of a common coupling point of an exchange grid between a microgrid and a large grid, minimum power of a common coupling point of an exchange grid, and maximum power of a common coupling point of an exchange grid;

[0119] S2. Calculating the operating cost of the microgrid control system based on the operating data;

[0120] S21. The operating cost of a microturbine is expressed as:

[0121]

[0122] In the formula, CMT (t) represents the operating cost of the microturbine at time t; G price represents the natural gas price; L gas Indicates the lower calorific value of natural gas; P MT (t) represents the output power of the microturbine at time t; η MT (t) represents the efficiency of the microturbine at time t; t represents time; In this embodiment, the lower calorific value of natural gas is set to 12.7 kWh / kg, and the natural gas price is set to 25.0 $ / m 3 ;

[0123] S22. The operating cost of a wind turbine is expressed as:

[0124]

[0125] In the formula, C WT (t) represents the operating cost of the wind turbine at time t; P WT (t) represents the output power of the wind turbine at time t; η WT (t) represents the efficiency of the wind turbine at time t; P v (t) represents the rated power of the fan at time t; v(t) represents the speed of the fan at time t; v ci Indicates wind shear velocity; v r Indicates rated wind speed; v co represents the wind shear speed; A, B and C represent parameters related to the characteristic curve of the wind turbine output power;

[0126] S23. The operating cost of a diesel engine is expressed as:

[0127] C DE (t) = a(P DE (t)) 2 +bP DE (t)+c;

[0128] In the formula, C DE (t) represents the operating cost of the diesel engine at time t; P DE (t) represents the output power of the diesel engine at time t; a, b and c represent the fuel cost coefficients of the diesel engine;

[0129] S24. The operating cost of photovoltaic power source is expressed as:

[0130]

[0131] In the formula, C PV (t) represents the operating cost of the photovoltaic power source at time t; P PV (t) represents the output power of the photovoltaic power source at time t; PrPV (t) represents the rated power value supplied to the photovoltaic power source at time t; η PV Represents the output efficiency of photovoltaic power source; P V (t) represents the rated power of the fan at time t; I F (t) represents the actual light intensity at sampling point t; t represents the temperature coefficient at sampling point t; T(t) represents the photovoltaic environment temperature at time t; T st (t) represents the temperature value tested under the same environment at time t;

[0132] S25. The operating cost of the fuel cell is expressed as:

[0133]

[0134] η FC (t) = 0.6735 - 0.0023 × P FC (t);

[0135] In the formula, C FC (t) represents the operating cost of the fuel cell at time t; P FC (t) represents the output power of the fuel cell at time t; η FC (t) represents the output efficiency of the fuel cell at time t; Δt represents the time period;

[0136] S26. The operating cost of the battery is expressed as:

[0137]

[0138] In the formula, C SB (t) represents the operating cost of the battery at time t; P SB (t) represents the output power of the battery at time t; P ESB,ch (t) represents the charging power at time t; P ESB,dis (t) represents the discharge power at time t; τ represents the variable rate coefficient of battery charge and discharge; represents the charging efficiency at time t; represents the discharge efficiency at time t;

[0139] S3, constructing a microgrid operation planning optimization model, wherein the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and constraints set based on the operation data;

[0140] S31, the objective function is expressed as:

[0141] minF=C MT +C WT +CDE +C PV +C FC +C SB ;

[0142] Among them, C MT represents the operating cost of the microturbine; C WT represents the operating cost of the wind turbine; C DE Represents the operating cost of a diesel engine; C PV Represents the operating cost of photovoltaic power source; C FC represents the operating cost of the fuel cell; C SB Indicates the operating cost of the battery;

[0143] S32, the constraints include output power constraints of distributed power sources, power load and power balance equality constraints, unit ramp rate limits and power interaction constraints;

[0144] The output power constraint of the distributed power source is expressed as follows:

[0145] P j,min ≤P j (t)≤P j,max ;

[0146] Where P j (t) represents the power output of the jth power supply at time t; P j,min represents the minimum active power output of the jth power supply; P j,max represents the maximum active power output of the jth power supply;

[0147] The power load and the output power constraint of the power balance are expressed as:

[0148] P load (t) = P MT +P WT +P DE +P PV +P FC +P SB +u·P EX ;

[0149] Where P load (t) represents the power load at time t; P EX Indicates the power of the common coupling point between the microgrid and the large grid; u indicates whether there is power exchange between the microgrid and the large grid. If so, the value is 1, otherwise, the value is 0;

[0150] The unit ramp rate limit is expressed as follows:

[0151]

[0152] In the formula, represents the upper limit of the output power of the microturbine; represents the upper limit of the wind turbine's output power; Indicates the upper limit of diesel engine output power; Indicates the upper limit of the output power of the photovoltaic power source; Indicates the upper limit of the output power of the fuel cell; Indicates the upper limit of the battery's output power;

[0153] The power interaction constraint is expressed as follows:

[0154] P EX,min ≤P EX (t)≤P EX,max ;

[0155] Where P EX (t) represents the power of the common coupling point between the microgrid and the large grid at time t; P EX,min Represents the minimum power of the common coupling point of the switching grid; P EX,max Indicates the maximum power of the common coupling point of the exchange grid;

[0156] S4, using the improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to solve the microgrid operation planning optimization model to obtain the microgrid operation planning scheme:

[0157] The improved Archimedean optimization algorithm includes an initialization phase, an attribute update phase, a conversion variable calculation phase, an exploration phase, a development phase, and an evaluation phase;

[0158] S41, the initialization stage specifically initializes the operation planning candidate schemes, the range of the operation planning candidate schemes, the density of the operation planning candidate schemes and the moving speed of the operation planning candidate schemes, which is expressed as:

[0159] x i =lb i +rand1()×(ub i -lb i ), i=1,2,...,N;

[0160] vol i =rand1();

[0161] den i =rand1();

[0162] acc i =lb i +rand1()×(ubi -lb i );

[0163] In the formula, x i represents the i-th candidate operation plan; N represents the number of candidate operation plan plans; rand1() represents a d-dimensional vector randomly generated in the interval [0, 1]; d represents the preset dimension; lb i represents the lower limit of the value of the candidate solution of the i-th operation plan; ub i represents the upper limit of the value of the i-th operation plan candidate; i represents the index value of the i-th operation plan candidate; vol i represents the range of candidate solutions for the i-th operation plan; i represents the density of the candidate plan for the i-th operation plan; acc i represents the moving speed of the i-th operation planning candidate;

[0164] S42, the updating attribute stage specifically calculates the objective function values ​​of all the operation planning candidate solutions, selects the operation planning candidate solution with the smallest objective function value as the current optimal operation planning solution, and updates the range and density of the operation planning candidate solutions, which can be expressed as:

[0165]

[0166] In the formula, represents the range of candidate solutions for the ith operation plan in the nth iteration; vol best represents the range of the best candidate solutions; Indicates the density of the candidate solutions for the ith operation plan in the nth iteration; vol best Indicates the density of the best candidate solution; n indicates the index value of the nth iteration;

[0167] S43, the calculation conversion variable stage, is expressed as follows:

[0168]

[0169] Where TF represents the transformation variable; n represents the index value of the nth iteration; n max represents the maximum number of iterations; exp() represents the exponential function; d represents the density factor;

[0170] S44, the exploration phase is specifically when TF≤0.5, there is a collision between the running planning candidate solutions; when TF>0.5, there is no collision between the running planning candidate solutions, and the moving speed of the running planning candidate solutions is updated, which is expressed by the formula:

[0171]

[0172] In the formula, represents the moving speed of the candidate solution of the ith operation plan in the nth iteration; mr Indicates the density of random planning candidate solutions; vol mr Indicates the range of random materials; acc mr represents the moving speed of the random planning candidate solution; mr represents the index value of the random planning candidate solution;

[0173] The moving speed of the i-th operation plan candidate solution of the n-th iteration is normalized and expressed as:

[0174]

[0175] In the formula, represents the normalized moving speed of the ith operation plan candidate of the nth iteration; μ represents the preset normalization range; λ represents the preset normalization range; max() represents the maximum value function; min() represents the minimum value function;

[0176] S45, the development stage specifically uses the reverse learning strategy to update the operation planning candidate solution and the multi-directional strategy to update the moving direction of the operation planning candidate solution, wherein:

[0177] The reverse learning strategy specifically calculates and evaluates the forward and reverse solutions of the operation plan candidate schemes at the same time, calculates the objective function values ​​of the forward and reverse solutions, and sorts them, and selects the operation plan candidate schemes whose objective function values ​​are within a preset range as the new operation plan candidate set, which is expressed in the formula:

[0178]

[0179] S new =S×α r ;

[0180] x i '=ub i +lb i -x i ;

[0181] S={x'1,x'2,...,x' N};

[0182] In the formula, S new represents the new operation plan candidate set; S represents the reverse solution set; x' i represents the i-th reverse solution; α r represents the adjustment coefficient of the new operation plan candidate set; L istarrepresents the distance between the preset ideal operation plan and the second best candidate plan; rand2() represents a random function; β represents the preset lower bound of the random function; γ represents the preset upper bound of the random function; in this embodiment, β is set to -0.5 and γ is set to 0.5;

[0183] Update the candidate operation plan, expressed as:

[0184]

[0185] T = TF * K3;

[0186] In the formula, represents the optimal operation plan at the nth iteration; K1 represents a preset constant; K2 represents a preset constant; R represents a random number in the interval [0, 1]; x R represents the Rth operation plan candidate; T represents the proportional variable; F new It represents the guiding factor that changes the moving direction of the operation planning candidate solution;

[0187] Among them, the moving direction of the candidate scheme of the operation planning is updated by using a multi-directional strategy, specifically adding random numbers to move the guidance factor in multiple directions, which can be expressed as:

[0188]

[0189] W = 2R-K4;

[0190] Where, K4 represents the preset constant; W represents the search range;

[0191] S46, the evaluation phase specifically calculates the corresponding objective function value of the operation planning candidate scheme after each iteration update, and updates the current optimal operation planning scheme; until the maximum number of iterations is reached or the objective function value converges, the operation planning scheme is obtained.

[0192] Example 2

[0193] This embodiment provides a microgrid operation planning system based on an improved Archimedean optimization algorithm, the system comprising a data acquisition module, an operation planning optimization model, an optimization solution module and a solution output module, wherein:

[0194] The data acquisition module is used to acquire the operation data of the microgrid control system, which includes a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the operation data includes micro turbine data, wind turbine data, diesel engine data, photovoltaic power source data, fuel cell data, battery data and related microgrid data; based on the operation data, the operation cost of the microgrid control system is calculated; the operation data and the operation cost are transmitted to the operation planning optimization model construction module; this module is used to implement the function of step S1 in embodiment 1, which will not be repeated here;

[0195] The operation planning optimization model construction module is used to construct a microgrid operation planning optimization model, and the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and a constraint condition set based on the operation data, and the constraint condition includes a distributed power supply output power constraint, a power load and power balance equality constraint, a unit climbing rate limit, and a power interaction constraint; this module is used to implement the function of step S2 in Example 1, which will not be repeated here;

[0196] The optimization solution module uses an improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to solve the microgrid operation planning optimization model to obtain a microgrid operation planning scheme; the microgrid operation planning scheme includes optimal planning parameters and optimal operating costs; this module is used to implement the function of step S3 in embodiment 1, which will not be repeated here;

[0197] The scheme output module is used to display the microgrid operation planning scheme. This module is used to implement the function of step S4 in Example 1 and will not be described in detail here.

[0198] Example 3

[0199] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any embodiment of the present invention is implemented.

[0200] Example 4

[0201] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any embodiment of the present invention is implemented.

[0202] It is worth noting that the system, electronic device and computer-readable storage medium described in the present invention are based on the same principles as the method described in Example 1, and will not be described in detail here.

[0203] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A microgrid operation planning method based on an improved Archimedean optimization algorithm is characterized in that: The method comprises: Acquiring operation data of a microgrid control system, the microgrid control system including a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the operation data including micro turbine data, wind turbine data, diesel engine data, photovoltaic power source data, fuel cell data, battery data and related microgrid data; calculating the operation cost of the microgrid control system based on the operation data; Constructing a microgrid operation planning optimization model, wherein the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and constraints set based on the operation data, wherein the constraints include distributed power output constraints, power load and power balance equality constraints, unit ramp rate limits, and power interaction constraints; The improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy is used to solve the microgrid operation planning optimization model and obtain the microgrid operation planning scheme.

2. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 1 is characterized in that: The method also includes performing data cleaning on the operation data, wherein the data cleaning includes processing missing values, abnormal values ​​and standardizing data formats.

3. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 2 is characterized in that: In the operating data: The micro-turbine data includes an output power of the micro-turbine, an upper limit of the output power of the micro-turbine, and an efficiency of the micro-turbine; The wind turbine data includes an output power of the wind turbine, an upper limit of the output power of the wind turbine, an efficiency of the wind turbine, a rated power of the wind turbine, a speed of the wind turbine, a wind shear speed, a rated wind speed, a wind shear speed and a parameter related to a characteristic curve of the output power of the wind turbine; The diesel engine data includes the output power of the diesel engine, the upper limit of the output power of the diesel engine and the fuel cost coefficient of the diesel engine; The photovoltaic power source data includes the output power of the photovoltaic power source, the upper limit of the output power of the photovoltaic power source, the rated power value of the photovoltaic power source, the output efficiency of the photovoltaic power source, the rated power of the fan, the actual light illumination of the sampling point, the temperature coefficient of the sampling point, the photovoltaic environment temperature value and the temperature value; The fuel cell data includes the output power of the fuel cell, the upper limit of the output power of the fuel cell and the output efficiency of the fuel cell; The battery data includes the output power of the battery, the upper limit of the output power of the battery, the charging power, the discharging power, the variable rate of charging and discharging, the charging efficiency and the discharging efficiency; The relevant microgrid data include natural gas price, lower calorific value of natural gas, power output of distributed power sources, minimum active power output of distributed power sources, maximum active power output of distributed power sources, power load, power of the common coupling point of the exchange grid between the microgrid and the large grid, minimum power of the common coupling point of the exchange grid and maximum power of the common coupling point of the exchange grid.

4. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 3 is characterized in that: The operating costs of the microgrid control system include: The operating cost of a microturbine is expressed as: In the formula, C MT (t) represents the operating cost of the microturbine at time t; G price represents the natural gas price; L gas Indicates the lower calorific value of natural gas; P MT (t) represents the output power of the microturbine at time t; η MT (t) represents the efficiency of the microturbine at time t; t represents time; The operating cost of a wind turbine is expressed as: In the formula, C WT (t) represents the operating cost of the wind turbine at time t; P WT (t) represents the output power of the wind turbine at time t; η WT (t) represents the efficiency of the wind turbine at time t; P v (t) represents the rated power of the fan at time t; v(t) represents the speed of the fan at time t; v ci Indicates wind shear velocity; v r Indicates rated wind speed; v co represents the wind shear speed; A, B and C represent parameters related to the characteristic curve of the wind turbine output power; The operating cost of a diesel engine is expressed as: C DE (t)=a(P DE (t)) 2 +bP DE (t)+c; In the formula, C DE (t) represents the operating cost of the diesel engine at time t; P DE (t) represents the output power of the diesel engine at time t; a, b and c represent the fuel cost coefficients of the diesel engine; The operating cost of photovoltaic power is expressed as: In the formula, C PV (t) represents the operating cost of the photovoltaic power source at time t; P PV (t) represents the output power of the photovoltaic power source at time t; P rPV (t) represents the rated power value supplied to the photovoltaic power source at time t; η PV Represents the output efficiency of photovoltaic power source; P v (t) represents the rated power of the fan at time t; I F (t) represents the actual light intensity at sampling point t; t represents the temperature coefficient at sampling point t; T(t) represents the photovoltaic environment temperature at time t; T st (t) represents the temperature value tested under the same environment at time t; The operating cost of a fuel cell is expressed as: or FC (t)=0.6735-0.0023×P FC (t); In the formula, C FC (t) represents the operating cost of the fuel cell at time t; P FC (t) represents the output power of the fuel cell at time t; η FC (t) represents the output efficiency of the fuel cell at time t; Δt represents the time period; The operating cost of the battery is expressed as: In the formula, C SB (t) represents the operating cost of the battery at time t; P SB (t) represents the output power of the battery at time t; P ESB,ch (t) represents the charging power at time t; P ESB,dis (t) represents the discharge power at time t; τ represents the variable rate coefficient of battery charge and discharge; represents the charging efficiency at time t; Represents the discharge efficiency at time t.

5. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 4 is characterized in that: The corresponding objective function is constructed with the goal of minimizing the operating cost, which can be expressed as follows: minF=C MT +C WT +C DE +C PV +C FC +C SB ; Where F represents the objective function; C MT represents the operating cost of the microturbine; C WT represents the operating cost of the wind turbine; C DE Represents the operating cost of a diesel engine; C PV Represents the operating cost of photovoltaic power source; C FC represents the operating cost of the fuel cell; C SB Indicates the operating cost of the battery.

6. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 5 is characterized in that: The output power constraint of the distributed power source is expressed as follows: P j,min ≤P j (t)≤P j,max ; Where P j (t) represents the power output of the jth power supply at time t; P j,min represents the minimum active power output of the jth power supply; P j,max represents the maximum active power output of the jth power supply; The power load and the output power constraint of the power balance are expressed as: P load (t)=P MT +P WT +P DE +P PV +P FC +P SB +u·P EX ; Where P load (t) represents the power load at time t; P EX Indicates the power of the common coupling point between the microgrid and the large grid; u indicates whether there is power exchange between the microgrid and the large grid. If so, the value is 1, otherwise, the value is 0; The unit ramp rate limit is expressed as follows: In the formula, represents the upper limit of the output power of the microturbine; represents the upper limit of the wind turbine's output power; Indicates the upper limit of diesel engine output power; Indicates the upper limit of the output power of the photovoltaic power source; Indicates the upper limit of the output power of the fuel cell; Indicates the upper limit of the battery's output power; The power interaction constraint is expressed as follows: P EX,min ≤P EX (t)≤P EX,max ; Where P EX (t) represents the power of the common coupling point between the microgrid and the large grid at time t; P EX,min Represents the minimum power of the common coupling point of the switching grid; P EX,max Indicates the maximum power at the point of common coupling of the switching grid.

7. The microgrid operation planning method based on the improved Archimedean optimization algorithm according to claim 6 is characterized in that: In the step of solving the microgrid operation planning optimization model by using the improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to obtain the microgrid operation planning scheme: The improved Archimedean optimization algorithm includes an initialization phase, an attribute update phase, a conversion variable calculation phase, an exploration phase, a development phase, and an evaluation phase, specifically: The initialization stage specifically initializes the candidate operation plans, the range of the candidate operation plans, the density of the candidate operation plans, and the moving speed of the candidate operation plans, which can be expressed as: x i =lb i +rand1()×(ub i -lb i ),i=1,2,...,N; full i =rand1(); the i rand1(); <h2 style=";text-align:left;direction:ltr">acc<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =lb<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> +rand1()×(ub<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> -lb<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ); In the formula, x i represents the i-th candidate operation plan; N represents the number of candidate operation plan plans; rand1() represents a d-dimensional vector randomly generated in the interval [0, 1]; d represents the preset dimension; lb i Indicates the lower limit of the value of the candidate solution of the i-th operation plan; ub i represents the upper limit of the value of the candidate solution of the i-th operation plan; i represents the index value of the i-th operation plan candidate; vol i represents the range of candidate solutions for the i-th operation plan; den i Indicates the density of the candidate scheme for the i-th operation plan; acc i represents the moving speed of the i-th operation planning candidate; The updating attribute stage specifically calculates the objective function values ​​of all the operation planning candidate solutions, selects the operation planning candidate solution with the smallest objective function value as the current optimal operation planning solution, and updates the range and density of the operation planning candidate solutions, which can be expressed as follows: In the formula, represents the range of candidate solutions for the ith operation plan in the nth iteration; vol best represents the range of the best candidate solutions; Indicates the density of the candidate solutions for the ith operation plan in the nth iteration; vol best Indicates the density of the best candidate solution; n indicates the index value of the nth iteration; The calculation conversion variable stage is expressed as follows: Where TF represents the transformation variable; n represents the index value of the nth iteration; n max represents the maximum number of iterations; exp() represents the exponential function; d represents the density factor; The exploration phase is specifically when TF≤0 .5, there is a collision between the running planning candidates; when TF>0.5, there is no collision between the running planning candidates, and the moving speed of the running planning candidates is updated, which is expressed as: In the formula, represents the moving speed of the candidate solution of the ith operation plan in the nth iteration; mr Indicates the density of random planning candidate solutions; vol mr Indicates the range of random materials; acc mr represents the moving speed of the random planning candidate solution; mr represents the index value of the random planning candidate solution; The moving speed of the i-th operation plan candidate solution of the n-th iteration is normalized and expressed as: In the formula, represents the normalized moving speed of the ith operation plan candidate of the nth iteration; μ represents the preset normalization range; λ represents the preset normalization range; max() represents the maximum value function; min() represents the minimum value function; The development stage specifically uses the reverse learning strategy to update the operation planning candidate solution and the multi-directional strategy to run the plan to update the moving direction of the operation planning candidate solution, wherein: The reverse learning strategy specifically calculates and evaluates the forward and reverse solutions of the operation planning candidate solutions at the same time, calculates the objective function values ​​of the forward and reverse solutions, and sorts them, and selects the operation planning candidate solutions whose objective function values ​​are within a preset range as the new operation planning candidate set, which is expressed in the formula: S new =S×α r ; x’ i =ub i +lb i -x i ; S={x’1,x'2,...,x' N }; In the formula, S new represents the new operation plan candidate set; S represents the reverse solution set; x i ' represents the i-th reverse solution; α r represents the adjustment coefficient of the new operation plan candidate set; L istar represents the distance between the preset ideal operation plan and the second best candidate plan; rand2() represents a random function; β represents the preset lower bound of the random function; γ represents the preset upper bound of the random function; Update the candidate operation plan, expressed as: T = TF * K3; In the formula, represents the optimal operation plan at the nth iteration; K1 represents a preset constant; K2 represents a preset constant; R represents a random number in the interval [0, 1]; x R represents the Rth operation plan candidate; T represents the proportional variable; F new It represents the guiding factor that changes the moving direction of the operation planning candidate solution; Among them, the moving direction of the candidate scheme of the operation planning is updated by using a multi-directional strategy, specifically adding random numbers to move the guidance factor in multiple directions, which can be expressed as: W = 2R-K4; Where, K4 represents the preset constant; W represents the search range; The evaluation phase specifically calculates the corresponding objective function value of the operation planning candidate scheme after each iterative update, and updates the current optimal operation planning scheme; until the maximum number of iterations is reached or the objective function value converges, the operation planning scheme is obtained.

8. The microgrid operation planning system based on the improved Archimedean optimization algorithm is characterized by: The system includes a data acquisition module, an operation planning optimization model, an optimization solution module and a solution output module, wherein: The data acquisition module is used to acquire the operation data of the microgrid control system, the microgrid control system includes a micro turbine, a wind turbine, a photovoltaic power source, a diesel engine, a fuel cell and a battery; the operation data includes micro turbine data, wind turbine data, diesel engine data, photovoltaic power source data, fuel cell data, battery data and related microgrid data; based on the operation data, the operation cost of the microgrid control system is calculated; the operation data and the operation cost are transmitted to the operation planning optimization model construction module; The operation planning optimization model construction module is used to construct a microgrid operation planning optimization model, and the microgrid operation planning optimization model includes constructing a corresponding objective function with the goal of minimizing the operation cost, and constraints set based on the operation data, and the constraints include distributed power output power constraints, power load and power balance equality constraints, unit climbing rate limits and power interaction constraints; The optimization solution module uses an improved Archimedean optimization algorithm based on reverse learning and multi-directional strategy to solve the microgrid operation planning optimization model to obtain a microgrid operation planning scheme; the microgrid operation planning scheme includes optimal planning parameters and optimal operation costs; The scheme output module is used to display the microgrid operation planning scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the microgrid operation planning method based on the improved Archimedean optimization algorithm as described in any one of claims 1 to 7 is implemented.

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