Grid-connected microgrid capacity optimization configuration method based on improved ant lion algorithm

Abstract

The present application relates to the grid-connected micro-grid capacity optimization configuration method based on improved ant lion algorithm, including the following steps: obtaining micro-grid wind / light resource data and load data, establishing distributed power supply and energy storage device mathematical model, formulating micro-grid operation strategy. With the minimum comprehensive investment cost as the planning target, the self-balancing rate, the tie-line power fluctuation rate and the renewable energy utilization rate as the evaluation indexes, the grid-connected micro-grid capacity optimization configuration model is established. The improved ant lion algorithm is used to solve the grid-connected micro-grid capacity optimization configuration model, and the optimal micro-grid capacity configuration scheme is obtained. The method has the advantages of fast calculation speed and high convergence progress, and can ensure that the micro-grid fully utilizes renewable energy and safely and economically operates, and has practical significance for micro-grid planning.

Description

Technical Field

[0001] This invention relates to the field of capacity optimization configuration of grid-connected microgrids, and in particular to a method for capacity optimization configuration of grid-connected microgrids based on an improved antlion algorithm. Background Technology

[0002] With the introduction of the "dual carbon target," my country's power energy system will shift from a fossil fuel-based model to a new energy-based model. Renewable energy sources such as photovoltaics and wind power have advantages such as flexible application and abundant resources, and have received significant attention and support from the government in recent years. However, distributed power sources, primarily based on renewable energy, pose challenges to the security and stability of the power system due to the inherent randomness and volatility of energy itself. Energy storage, with its flexible charging and discharging switching modes and rapid power regulation characteristics, can effectively solve these problems. Therefore, integrated wind-solar-storage microgrid technology has been vigorously developed. Rationally configuring the power supply and energy storage battery capacity within a microgrid is a crucial step in effectively suppressing fluctuations in wind and solar power output, and is also of great significance in ensuring the economic and stable operation of the system.

[0003] In terms of model solving, genetic algorithms, particle swarm algorithms, internal search algorithms, ant colony algorithms, wolf pack algorithms, etc., are prone to getting stuck in local optima, slow convergence speed, and poor accuracy when solving microgrid optimization configuration. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a microgrid capacity optimization configuration method based on an improved antlion algorithm.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for optimizing the capacity configuration of grid-connected microgrids based on an improved antlion algorithm, the specific steps of which are as follows:

[0007] (1) Obtain microgrid wind / solar resource data and load data

[0008] (2) Establish a distributed power source and energy storage system model. The grid-connected microgrid system structure includes distributed power sources and energy storage systems. Distributed power sources include photovoltaic and wind power systems. Photovoltaic, wind power and energy storage systems are all connected to the AC bus. The AC bus interacts with the main grid and supplies power to the load at the same time.

[0009] (3) A grid-connected microgrid capacity optimization configuration model is established with the minimum comprehensive investment cost as the planning objective function and the self-balancing rate, tie-line power fluctuation rate, and renewable energy utilization rate as evaluation indicators.

[0010] (4) The improved antlion algorithm is used to solve the grid-connected microgrid capacity optimization configuration model to obtain the optimal configuration scheme.

[0011] The objective function f of the grid-connected microgrid capacity optimization configuration model is specifically described as follows:

[0012] f(cost) = min(C) yw +C js +C s )

[0013] In the formula, C js For investment costs; C yw For maintenance costs; C s Cost of purchasing and selling electricity.

[0014] The specific description of the constraints of the grid-connected microgrid capacity optimization configuration model is as follows:

[0015]

[0016] In the formula: P pv ,P wind ,P ess P represents the power output of photovoltaic, wind power, and energy storage systems, respectively. load For demand load, P gird-in It is the power input from the large power grid to the microgrid, i.e., the purchased power, P gird-out P is the power output from the microgrid to the main grid, i.e., the electricity sold. ess (t) represents the power output of the stored energy at time t, P gird (t) represents the interaction power between the microgrid and the upper-level grid; SOC(t) represents the state of charge of the battery pack at time t during the charging and discharging process; SOC(0) represents the initial state of charge of the battery; SOC(t) end P represents the final state of charge of the battery; ess The battery system's charging and discharging power is represented by α and β, respectively; the battery system's charging and discharging efficiency is represented by E. e The rated capacity of the battery pack is Δt; the sampling interval is 1 hour in this paper. The superscripts max and min represent the maximum and minimum values ​​of the corresponding data, respectively.

[0017] The evaluation indicators for the grid-connected microgrid capacity optimization configuration model are specifically described as follows:

[0018]

[0019] R s E represents the self-balancing rate of the microgrid. s E represents the microgrid's own power generation. t This represents the total electricity demand of the load.

[0020]

[0021] V lineP represents the power fluctuation rate of the tie line. line,i It is the fluctuation of the power of the connecting line at any moment. Let n be the average power of the tie line, and n be one calculation period.

[0022]

[0023] R ren For renewable energy utilization rate, E DG For renewable energy generation, E a This refers to the amount of electricity wasted.

[0024] An improved antlion algorithm is used to solve the grid-connected microgrid capacity optimization configuration model to obtain the optimal configuration scheme. The improved algorithm first uses chaotic mapping to generate an initial population, then uses a spiral convergence method to enhance population diversity and improve algorithm convergence, and finally uses a dynamic scaling factor to improve the algorithm's search capability, thereby comprehensively improving the algorithm's optimization performance.

[0025] The improved antlion algorithm includes the following specific steps:

[0026] Step 1: Set the population size N, particle variable dimension D, and maximum number of iterations k. max The population space is initialized using chaotic mapping;

[0027] Step 2: Calculate the fitness value of the entire population based on the objective function, and name the antlion with the best fitness value in the antlion population as the elite antlion.

[0028] Step 3: Use a roulette wheel to randomly match each ant with an antlion. Update the upper and lower bounds of the walking range based on the position of the matched antlion. Let the ant walk randomly near the selected antlion and the elite antlion according to the random walk formula. Then, take the weighted value according to the elite strategy with dynamic proportional coefficient. If the ant is captured by the antlion, slide to the corresponding antlion through a spiral convergence method.

[0029] Step 4: Determine the fitness value of all ants. If the fitness value of an ant is greater than that of the antlion, then update the position of the antlion.

[0030] Step 5: Determine if the maximum number of iterations has been reached. If it has, output the final result. If the maximum number of iterations has not been reached, repeat Step 3.

[0031] The specific steps of the spiral convergence method are as follows:

[0032]

[0033] In the formula: D' is the distance between ant i and the ant lion that captured it; This represents the position of the antlion that captured the ant in the kth iteration. This represents the position of the i-th ant during the k-th iteration.

[0034]

[0035] In the formula: Let b be the position of the i-th ant in the (k+1)-th iteration, b be a constant, δ be a random number between [-1, 1], and k be the current iteration number. max This represents the maximum number of iterations.

[0036] The formula for the dynamic scaling factor ε is:

[0037]

[0038] The ant's position in the k-th iteration is:

[0039] in, Let ε be the position of the i-th ant in the k-th iteration. max ,ε min These are the maximum and minimum values ​​of the dynamic scaling factor, respectively, where k is the current iteration number and k is the maximum iteration number.

[0040] max

[0041] Preferably, ε max ,ε min Set them to 0.85 and 0.35 respectively; b is 0.5.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] This invention utilizes an improved antlion algorithm for microgrid power capacity optimization, with the goal of minimizing overall investment cost. Evaluation indicators include self-balancing rate, tie-line power fluctuation rate, and renewable energy utilization rate. A grid-connected microgrid capacity optimization model is established, and the resulting optimization improves the renewable energy absorption capacity while ensuring the safe, reliable, economical, and efficient operation of the microgrid. Attached Figure Description

[0044] Figure 1 Schematic diagram of grid-connected microgrid structure.

[0045] Figure 2 Microgrid operation strategy flowchart.

[0046] Figure 3 Improved Antlion Algorithm Flowchart. Detailed Implementation

[0047] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the scope of protection of the present application.

[0048] Microgrid power model

[0049] (1) Wind turbine output model

[0050] The operating state of a wind turbine is closely related to the magnitude of the wind speed v, and the relationship is described as follows:

[0051]

[0052] In the formula: The rated power of the fan; v ci v r and v co These are the inlet velocity, rated velocity, and outlet velocity P of the fan. w This represents the actual output power of the fan.

[0053] (2) Photovoltaic array output model

[0054] The operating status of photovoltaic modules is closely related to light intensity, ambient temperature, and rated power under standard conditions. The relationship is described as follows:

[0055]

[0056] In the formula: P PV P represents the actual output power of the photovoltaic module. STC G represents the rated output power of the photovoltaic module. AC δ represents the light intensity; δ represents the power temperature coefficient; ΔT represents the difference between the actual operating temperature and the reference temperature (25℃); G STC The light intensity is under standard test conditions.

[0057] (3) Battery pack model

[0058] Batteries have two states: charging and discharging. Their operation process is as follows.

[0059] Charging process:

[0060]

[0061] Discharge process:

[0062]

[0063] In the formula: SOC(t) is the state of charge of the battery pack at time t during the charging and discharging process; SOC(t-1) is the state of charge of the battery pack at time t-1 during the charging and discharging process; P bat,c(t), P bar,dis (t) represent the charging and discharging efficiencies of the battery system, respectively; α and β represent the charging and discharging efficiencies of the battery system, respectively; E e Δt represents the rated capacity of the battery pack; Δt is the sampling interval, which is 1 hour in this embodiment.

[0064] Establishment of a capacity optimization configuration model for grid-connected microgrids

[0065] A grid-connected microgrid capacity optimization model is established to achieve the optimal overall cost of equipment investment, operation and maintenance, and grid interaction within the system. The objective function expression is as follows:

[0066] f(cost) = min(C) yw +C js +C s )

[0067] In the formula, C js For investment costs; C yw For operation and maintenance costs; C s This refers to the cost of grid interaction.

[0068] C yw =(k pv-yw P pv +k wind-yw P wind +k ess-yw P ess ) / 365l

[0069]

[0070]

[0071] In the formula: C yw For operation and maintenance costs, P pv ,P wind ,P ess These represent the installed capacity, or power output, of photovoltaic, wind power, and energy storage systems, respectively; k pv-yw k wind-yw k ess-yw The operation and maintenance coefficients for photovoltaic, wind power, and battery storage are C, respectively. js Where C is the investment cost, r is the inflation rate, and l is the equipment's useful life; s For the cost of purchasing and selling electricity, k pv-js k ess-js k wind-js The respective investment costs for each photovoltaic, wind power, and battery unit, k grid-in k grid-out These are the purchase and sale prices of electricity, P grid-in P grid-out These refer to the power purchased and the power sold, respectively.

[0072] The following constraints must be met when a microgrid system is running:

[0073]

[0074] In the formula: P pv ,P wind ,P ess The power outputs of the photovoltaic, wind power, and energy storage systems are respectively, P load For demand load, P gird-in It is the power input from the large power grid to the microgrid, i.e., the purchased power, P gird-out P is the power output from the microgrid to the main grid, i.e., the electricity sold. ess (t) represents the power output of the stored energy at time t, P gird (t) represents the interaction power between the microgrid and the upper-level grid; SOC(t) represents the state of charge of the battery pack at time t during the charging and discharging process; SOC(0) represents the initial state of charge of the battery; SOC(t) end P represents the final state of charge of the battery; ess E represents the charging and discharging power of the energy storage system; α and β represent the charging and discharging efficiency of the battery system, respectively; e Δt represents the rated capacity of the battery pack; Δt is the sampling interval, which is 1 hour in this embodiment; the superscripts max and min are the maximum and minimum values ​​of the corresponding data, respectively.

[0075] Evaluation Indicators for Grid-Connected Microgrid Capacity Optimization Configuration Model

[0076] While fully considering economic indicators, a comprehensive consideration of various technical indicators of the microgrid is also necessary to ensure maximum renewable energy absorption capacity and safe and reliable operation of the microgrid. This invention takes into account the microgrid's self-balancing rate, tie-line power fluctuation rate, and renewable energy utilization rate.

[0077] (1) Microgrid self-balancing rate

[0078] The microgrid self-balancing rate reflects the degree to which a microgrid depends on the main power grid.

[0079]

[0080] R s E represents the self-balancing rate of the microgrid. s E represents the microgrid's own power generation. t This represents the total electricity demand of the load.

[0081] (2) Connection line power fluctuation

[0082] The tie-line power fluctuation rate of a microgrid is an important indicator for measuring the impact of the main grid on the microgrid. Excessive tie-line power fluctuation can affect the safety and stability between the microgrid and the main grid.

[0083]

[0084] V line P represents the power fluctuation of the tie line. line,i It is the power fluctuation of the tie line at time i. Let n be the average power of the tie line, and n be one calculation period.

[0085] (3) Renewable energy utilization rate

[0086]

[0087] R ren For renewable energy utilization rate, E DG For renewable energy generation, E a This refers to the amount of electricity wasted.

[0088] Microgrid operation strategy

[0089] When wind turbines and photovoltaic power generation exceed the load demand, electricity is sold during peak periods and batteries are charged during off-peak periods.

[0090] When wind turbines and photovoltaic power generation are insufficient, batteries discharge during peak periods and electricity is purchased during off-peak periods; electricity can be sold to the main grid as appropriate to improve the economic efficiency of microgrid power generation.

[0091] The specific microgrid operation strategy process is as follows: First, read wind / solar resource data and load data, determine the current time, and judge the load change ΔP. load If the value is less than 0, determine if it is the off-peak electricity price. If so, charge the battery and send the excess power to the main grid. If not, send the excess power directly to the main grid and end.

[0092] If the value is not less than 0, determine whether it is off-peak electricity pricing. If so, purchase electricity from the main grid, and any surplus can be used to charge the battery. If not, determine the load change ΔP. load Is it less than or equal to the maximum power output of the energy storage system? If so, the battery discharges, and the excess power is supplied to the main grid; otherwise, electricity is purchased from the main grid, and the process ends.

[0093] Improved Antlion Algorithm

[0094] The aforementioned grid-connected microgrid capacity optimization configuration model is solved using an improved antlion algorithm to obtain the optimal configuration scheme. The improved antlion algorithm first utilizes chaotic mapping to generate an initial population, then employs a spiral convergence method to enhance population diversity and improve algorithm convergence, and finally uses a dynamic scaling factor to improve the algorithm's search capability, thereby comprehensively improving the algorithm's optimization performance.

[0095] (1) Cubic mapping generates the initial population.

[0096] The original antlion algorithm uses a random generation method to produce the initial population, which is not conducive to improving the algorithm's global search capability. The improved antlion algorithm uses a cubic mapping to generate the initial population, initializes the number of iterations, population size, ant and antlion populations, and calculates the fitness value.

[0097] Within the interval [0,1], randomly generate a sequence x and substitute it into the following formula:

[0098]

[0099] -1≤y n ≤1, n=1,2...,y n ≠0

[0100]

[0101] In the formula: y n It is a chaotic sequence, where n is the number of mappings; It returns to the position in the solution space and then assigns it to an ant or antlion individual; ub and lb are the upper and lower bounds of the solution space, respectively.

[0102] (2) Random roaming of ants

[0103] The random walk process of ants can be viewed as the search process of each search agent on the feasible domain:

[0104] X(k)=[0,cumsum(2r(k1)-1),...,cumsum(2r(k) max )-1)]

[0105]

[0106] In the formula: X(k) represents the random walk of the ants; cumsum represents the cumulative sum of the step lengths of the random walk of the ants; r(k) is a random function; k is the number of iterations, k1 is the first iteration, and k... max `r` represents the maximum number of iterations; `rand` is a random number uniformly distributed on [0,1]. During the iteration process, the positions and fitness of the ants and antlions are stored in corresponding matrices.

[0107] Normalization is performed to ensure that ants can move within a feasible range.

[0108]

[0109] In the formula: Let a represent the position of the i-th ant in the k-th iteration. i b i Let and be the minimum and maximum values ​​for the i-th ant's movement, respectively. Let i and j represent the maximum and minimum values ​​of the i-th ant in the k-th iteration, respectively, where i+j=N.

[0110] (3) Trapping ants

[0111] Antlion traps affect the random movement of ants, mathematically represented as:

[0112]

[0113] In the formula: c k d k These are the maximum and minimum values ​​of all ants at the k-th iteration, respectively; Let be the position of the j-th antlion in the k-th iteration.

[0114] (4) Ants spiral and converge, slipping into a trap.

[0115] Each ant can only be captured by one antlion. A roulette wheel strategy is used to select the antlion for each ant. Furthermore, once an ant falls into the trap constructed by the antlion, the antlion will throw sand outwards, drastically reducing the ant's random wandering range and making escape more difficult. To enhance the probability of the antlion capturing the ant and improve the algorithm's usability, a spiral convergence approach is used to improve the convergence method after an ant falls into the antlion's trap.

[0116]

[0117] In the formula: D' is the distance between ant i and the ant lion that captured it; This represents the position of the antlion that captured the ant in the kth iteration. This represents the position of the i-th ant during the k-th iteration.

[0118]

[0119] In the formula: Let be the position of the i-th ant in the (k+1)-th iteration, b be a constant (0.5), δ be a random number between -1 and 1, and k be the current iteration number. max This represents the maximum number of iterations.

[0120] When the fitness of a certain ant Higher fitness than antlions If the antlion is captured, it is considered to have been captured. At this point, the antlion will update its position, which can be mathematically represented as:

[0121]

[0122] In the formula: Let j be the position of the j-th antlion in the k-th iteration. Let be the position of the i-th ant in the k-th iteration.

[0123] (5) Elite Strategy

[0124] The antlion with the highest fitness in each generation is considered the elite antlion. The elite antlion, along with the antlion selected through roulette, together influence the movement of ants.

[0125]

[0126] In the formula: The value generated by the ant randomly walking around the vicinity of the antlion selected in the kth generation; The value generated by the k-th generation of ants randomly walking around the elite ant lion.

[0127] To improve the algorithm's search capability, the elite strategy is modified by proposing a dynamic scaling factor ε, which assigns different weights to the two walking methods at different stages. In the early stages of iteration, the dynamic scaling factor decreases slowly, which is beneficial for global search; in the later stages of iteration, the decreasing rate increases, which is beneficial for local search.

[0128]

[0129]

[0130] Where ε max ,ε min , where are the maximum and minimum values ​​of the dynamic scaling factor, set to 0.85 and 0.35 respectively, k is the current iteration number, and k is the maximum iteration number.

[0131] max

[0132] Case Analysis

[0133] The basic parameters of the microgrid are shown in Table 1:

[0134] Table 1 Microgrid System Parameters

[0135]

[0136] Time-of-use electricity prices are shown in Table 2 below:

[0137] Table 2 Time-of-use Electricity Prices

[0138]

[0139] Table 3 Algorithm Optimization Results

[0140]

[0141] Increasing the installed capacity of grid-connected microgrids can effectively improve the self-balance rate R of microgrids. s However, excessively increasing the installed capacity of wind-solar-storage microgrids, while improving the microgrid self-balancing rate and renewable energy utilization rate in Scheme 4, significantly reduces economic efficiency and leads to increased power fluctuations in tie lines, making it counterproductive. Therefore, Scheme 3 is the optimal configuration choice.

[0142] Since the current purchase cost of microgrid equipment is relatively high, the electricity price of microgrids can be appropriately increased. This would increase the installed capacity of grid-connected microgrids, improve the self-balancing rate and the utilization rate of renewable energy, and enhance the economic efficiency of grid-connected microgrids.

[0143] The preferred embodiments of the present invention have been described in detail above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention.

[0144] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A method for optimizing the capacity configuration of grid-connected microgrids based on an improved antlion algorithm, characterized in that, Includes the following steps: (1) Acquire microgrid wind / solar resource data and load data (2) Establish a distributed power source and energy storage system model (3) A grid-connected microgrid capacity optimization configuration model is established with the minimum comprehensive investment cost as the planning objective function and the self-balancing rate, tie-line power fluctuation rate, and renewable energy utilization rate as evaluation indicators. (4) Formulate microgrid operation strategies When wind turbines and photovoltaic power generation exceed the load demand, electricity is sold during peak periods and batteries are charged during off-peak periods. When wind turbines and photovoltaic power generation are insufficient, batteries discharge during peak periods and electricity is purchased during off-peak periods; electricity is sold to the main grid as appropriate to improve the power generation economy of microgrids; (5) The improved antlion algorithm is used to solve the grid-connected microgrid capacity optimization configuration model to obtain the optimal configuration scheme; the improved algorithm uses chaotic mapping to generate an initial population, adopts a spiral convergence method to enhance population diversity, and adopts a dynamic scaling factor to improve the algorithm's search capability. The specific steps of the spiral convergence method are as follows: ， In the formula: For ants The distance to the antlion that captured it; This represents the position of the antlion that captured the ant in the kth iteration. For the first The position of the ant in the kth iteration; ， In the formula: For the (k+1)th iteration, the... The position of the ant, where b is a constant. A random number between [-1, 1] This represents the current iteration number. This represents the maximum number of iterations. The dynamic scaling factor The formula is: ， The ant's position in the k-th iteration is: ; in, These represent the maximum and minimum values ​​of the dynamic scaling factor, respectively, where k is the current iteration number. The maximum number of iterations; The specific description of the grid-connected microgrid capacity optimization configuration model is as follows: (1) Objective function f (cos t ): ， In the formula, For investment costs; For operation and maintenance costs; Cost of purchasing and selling electricity; (2) Constraints ， In the formula: , , These represent the power outputs of photovoltaic, wind power, and energy storage systems, respectively. For demand load, It refers to the power input from the large power grid to the microgrid, i.e., the purchased power. It is the power output from the microgrid to the main grid, i.e., the electricity sold. The power output at time t is the energy storage capacity. This refers to the power exchange between the microgrid and the upper-level power grid. This represents the state of charge of the battery pack at time t during the charging and discharging process. This represents the initial state of charge of the battery. This refers to the final state of charge of the battery. Indicates the charging and discharging power of the energy storage system; , These refer to the charging and discharging efficiency of the battery system; This refers to the rated capacity of the battery pack. The sampling interval is defined by the superscripts max and min, which represent the maximum and minimum values ​​of the corresponding data, respectively. (3) Evaluation indicators 1) Microgrid self-balancing rate: , In the formula: For the self-balance rate of the microgrid, For the microgrid's own power generation, This represents the total electricity demand of the load. 2) Tie-line power fluctuation rate: , In the formula: This refers to the power fluctuation of the tie line. yes The power fluctuation of the connecting line at any time, The average power of the tie line, One calculation cycle; 3) Renewable energy utilization rate: , In the formula: For renewable energy utilization rate, For renewable energy generation, This refers to the amount of electricity wasted.

2. The method for optimizing the capacity configuration of grid-connected microgrids according to claim 1, characterized in that, Set them to 0.85 and 0.35 respectively; b is 0.

5.

3. The method for optimizing the capacity configuration of grid-connected microgrids according to claim 1, characterized in that, The initial population is generated using chaotic mapping, employing a cubic mapping approach.

Images