Multi-enterprise micro-grid energy coordination optimization method and electronic equipment

Abstract

The invention relates to a multi-enterprise micro-grid energy coordination optimization method and electronic equipment, and the method comprises the steps: building a multi-enterprise micro-grid coordination optimization framework, and carrying out the information and power interaction between micro-grids; modeling internal resources of the enterprise microgrid, wherein the internal resources comprise a photovoltaic power generation model, a wind power generation model, an energy storage model, an electricity price model, a demand response model and a carbon transaction model; target functions and constraint conditions of single-enterprise micro-grid energy optimization and multi-enterprise micro-grid energy collaborative optimization are constructed respectively; and solving the problem of multi-enterprise micro-grid energy collaborative optimization by adopting an improved white whale optimization algorithm, outputting an optimal scheduling result of the multi-enterprise micro-grid, and scheduling internal resources of each enterprise micro-grid according to the optimal scheduling result. Compared with the prior art, the method has the advantages of considering a demand response mechanism and a carbon transaction mechanism, improving convergence performance and overall scheduling economy and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise microgrid economic dispatching, and in particular to a multi-enterprise microgrid energy coordination optimization method and electronic equipment. Background Art

[0002] In recent years, with the dual carbon goals as the main guide, it is imperative to build a new power system with new energy as the main body, and the grid connection and consumption of a large number of new energy sources have become important issues to be studied in scientific research. As part of the new power system, microgrids have distributed power sources, energy storage systems, etc., which can promote the consumption of new energy and the efficient use of resources.

[0003] The operation modes of microgrids can be divided into two modes: grid-connected and islanded. In the islanded mode, the microgrid has no power support from the large grid and only needs to achieve a balance between energy demand and load demand; however, due to the early capacity planning of new energy and the instability of wind and solar resources, it is difficult to achieve a balance between source and load. Therefore, it is possible to consider opening up information and resource interaction between multiple microgrids, promoting the local consumption of multiple distributed energy sources, and increasing the stability of the power system.

[0004] The existing multi-microgrid system ignores the demand response and carbon trading mechanism of future development, and the optimization algorithm used for solving the problem is prone to falling into local extreme values ​​and cannot quickly obtain the optimal solution.

[0005] After searching, the Chinese invention patent application publication number CN116307505A discloses a method for optimizing the energy and economic dispatching of enterprise microgrids, which includes the following steps: including: analyzing the operating characteristics of the enterprise microgrid power generation and power consumption system, establishing the operating models of each system; according to the system operating model, combined with the historical operating data of each system, using the optimal prediction algorithm to predict the power generation and power consumption; according to the power prediction results of the microgrid system, combined with the operating costs and constraints of each system, establishing the multi-objective function of the microgrid based on the day-ahead economic and environmental protection, safe and reliable, using the improved genetic algorithm to solve, and obtaining the optimal dispatching plan for the day; based on the day-ahead optimal dispatching plan, combined with the short-term prediction results, establishing the intra-day optimization objective function and constraints, and obtaining the intra-day optimal dispatching plan; issuing the intra-day optimal dispatching plan to each system device, so as to achieve the safe, reliable and economically optimal operation of the microgrid. This existing patent application has the problem of not considering demand response and carbon trading mechanism, and not considering the energy coordination between multiple microgrids.

[0006] After searching, China's invention patent application publication number CN118378752A discloses a microgrid grid-connected operation optimization scheduling method based on the improved Beluga optimization algorithm. However, it also has the problem of not considering demand response and carbon trading mechanism, and not considering energy coordination among multiple microgrids.

[0007] How to consider demand response and carbon trading to achieve energy coordination and optimization of multi-enterprise microgrids has become a technical problem that needs to be solved. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a multi-enterprise microgrid energy coordination optimization method and electronic equipment.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] According to one aspect of the present invention, a multi-enterprise microgrid energy coordination optimization method is provided, the method comprising the following steps:

[0011] Step S1: Establish a multi-enterprise microgrid coordination optimization framework, and exchange information and power between microgrids;

[0012] Step S2: Modeling the internal resources of the enterprise microgrid, including photovoltaic power generation model, wind power generation model, energy storage model, electricity price model, demand response model and carbon trading model;

[0013] Step S3: Based on the multi-enterprise microgrid coordinated optimization framework in step S1 and the model in step S2, objective functions and constraints for single-enterprise microgrid energy optimization and multi-enterprise microgrid energy collaborative optimization are constructed respectively;

[0014] Step S4: using the improved Beluga optimization algorithm to solve the problem of multi-enterprise microgrid energy collaborative optimization, and outputting the optimal dispatching result of the multi-enterprise microgrid;

[0015] Step S5: dispatch the internal resources of each enterprise microgrid according to the optimal dispatch result.

[0016] Preferably, in the multi-enterprise microgrid coordinated optimization framework, each microgrid is regarded as a user that sells or purchases electricity from other microgrids.

[0017] Preferably, the photovoltaic power generation model is specifically:

[0018]

[0019] Where P pv is the active power output of the photovoltaic cell; R pv is the photovoltaic output power under standard conditions; q pv is the efficiency coefficient of photovoltaic power generation; I T I is the irradiance parameter value actually adopted by the small meteorological station in the current area; STC is the radiation intensity parameter value under the standard test environment; α p is the temperature coefficient of the photovoltaic panel; T cis the photovoltaic cell temperature under current conditions; T stc is the photovoltaic cell temperature under standard test environment;

[0020] The wind power generation model is specifically as follows:

[0021]

[0022] Where P WT Represents the output power of the wind turbine; P r Indicates the rated power of the wind turbine; v, v ci 、v r and v co are actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed respectively; a, b, c and d are wind speed parameters;

[0023] The energy storage model is specifically:

[0024]

[0025] In the formula, S OC (t) is the remaining power ratio of the energy storage battery at time t; P bess (t) is the charging and discharging power of the energy storage battery, P bess (t) is discharged when positive and charged when negative; η - is the charging efficiency, η + is the discharge efficiency.

[0026] Preferably, the electricity price model is specifically:

[0027]

[0028] In the formula, C G (t) is the power purchase amount of the microgrid system at time t, P G (t) is the power purchase power of the microgrid system at time t, A, B, and C are all power generation cost coefficients, and σ is the unit price of electricity;

[0029] The demand response model is specifically as follows:

[0030] P Le (t) = P Le0 (t)+P cut (t)+P tran (t)

[0031] Where P Le (t) is the load after demand response at time t, P Le0 (t) is the original load at time t, P cut (t) is the load reduction at time t, P tran (t) is the load transferred at time t;

[0032] The carbon trading model is specifically as follows:

[0033] C co2 =E money ·(E all -H.P buy )

[0034] oeLh all =α·S buy +β·S rew

[0035] In the formula, C co2 is the carbon trading amount, E all is the total carbon emission quota, H is the carbon emission coefficient, P buy is the amount of electricity purchased from the power grid, E money is the unit price of carbon trading, S buy To purchase electricity from the large power grid, S rew is the electricity generation degree of renewable energy, α and β are the carbon quota coefficients.

[0036] Preferably, the objective function of the single-enterprise microgrid energy optimization is specifically:

[0037] min C i =min(C i,storage +C i,demand ,C i,co2 +C i,grid +C i,exc )

[0038] In the formula, C i is the total operating cost of the i-th microgrid; C i,storage is the charging and discharging cost of energy storage; C i,demand is the compensation cost of demand response, which is the sum of the compensation cost of load reduction and the compensation cost of load transferability; C i,co2 is the carbon trading cost; C i,grid is the transaction cost between the i-th microgrid and the large grid. i,grid When it is positive, it means that the microgrid buys electricity from the large grid, otherwise, it means that the microgrid sells electricity to the large grid; C i,exc is the sum of the interaction costs between the i-th microgrid and other microgrids;

[0039] The objective function of the multi-enterprise microgrid energy collaborative optimization is specifically:

[0040]

[0041] In the formula, C i,all is the total cost of operating the multi-microgrid system, that is, the sum of the operating costs of multiple microgrids; N is the number of microgrids.

[0042] Preferably, the constraints include source-load power balance constraints, energy storage output constraints, SOC constraints and interactive power constraints;

[0043] The source-load power balance constraint is specifically:

[0044] P i d +P i demand +P i exc =P i rew +P i ESS +P i G

[0045] Among them, P i d is the original load power; P i demand P is the sum of curtailable power and transferable power participating in demand response; i exc The interaction power between the i-th microgrid and other microgrids; P i rew is the power generation of new energy; P i ESS is the charging and discharging power of energy storage; P i G is the interaction power between the i-th microgrid and the large grid;

[0046] The energy storage output constraint and SOC constraint are:

[0047]

[0048] In the formula, is the energy storage charging and discharging power P i ESS The lower limit of is the energy storage charging and discharging power P i ESS The upper limit of For energy storage SOC i The lower limit of For energy storage SOC i upper limit.

[0049] More preferably, the interactive power constraint includes the interactive power constraint between the microgrid and the large grid, and the interactive power constraint between microgrids, specifically:

[0050]

[0051] In the formula, and are the interaction power P between microgrid i and the large grid respectively. i G The upper and lower limits of and are the interaction power P between microgrid i and other microgrids respectively. i exc upper and lower limits.

[0052] Preferably, a new population initialization strategy and a reverse learning strategy are used to improve the White Whale optimization algorithm. The new population initialization strategy is specifically:

[0053]

[0054] Where, X T and X T+1 are the individual positions before and after the new type of initialization, respectively;

[0055] The reverse learning strategy is specifically:

[0056] X′ i =(ub+lb)×θ-X i

[0057] In the formula, ub and lb are the upper and lower limits of the search space, θ is a random number between 0 and 1, and X i Indicates the current individual's position; X i 'Indicates the reverse solution of the current position.

[0058] More preferably, the process of step S4 includes:

[0059] Step S41, initializing parameters;

[0060] Step S42, initializing the population using a new population initialization strategy;

[0061] Step S43, calculating the fitness values ​​of individual beluga whales and selecting the individual with the best fitness;

[0062] Step S44, calculate the balance factor B f and whale fall probability W f ;

[0063] Step S45, if B f If it is less than 0.5, it is the development stage, otherwise it is the exploration stage; execute step S46;

[0064] Step S46, if B f Less than W f, then it is the whale fall stage, otherwise it is the reverse learning strategy; execute step S47;

[0065] Step S47, updating the fitness to determine the best individual;

[0066] Step S48: if the maximum number of iterations is reached, the multi-microgrid coordinated optimization result is output; otherwise, the process returns to step S44 until the maximum number of iterations is reached.

[0067] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1) The present invention breaks through the dispatching mode of traditional power grids, takes into account the demand response mechanism of virtual power plants and the carbon trading mechanism between microgrids, and establishes an economic dispatching model with the total operating cost of the microgrid group as the objective function. At the same time, considering that the traditional Beluga optimization algorithm is prone to fall into local extreme values ​​and has low convergence accuracy when solving optimization problems, a new initialization strategy and a reverse learning strategy are introduced, and the improved Beluga optimization algorithm is used to solve the energy coordination optimization of multi-enterprise microgrids, which improves the convergence performance of the algorithm. Compared with the microgrid of a single enterprise, the economic optimization benefits of the microgrid group are higher, which improves the overall economy.

[0070] 2) In the multi-enterprise microgrid coordination optimization framework constructed by the present invention, information and power are exchanged between microgrids, and each microgrid is regarded as a user who sells or purchases electricity from other microgrids. When the load is low and wind and solar power generation is large, the excess electricity is sent to the large power grid, or interacts with other microgrids or charges energy storage devices; when the load is high and wind and solar power generation is insufficient, electricity can be purchased from other microgrids, or energy storage can be discharged to seek maximum benefits. Compared with the traditional multi-microgrid without interaction, the economic dispatchability of the microgrid group is improved and the stability of the power system is increased.

[0071] 3) The improved White Whale optimization algorithm in the present invention improves the convergence performance of the algorithm, can quickly solve the optimal solution, and is conducive to the rapid adjustment of scheduling, thereby reducing the overall total operating cost and achieving maximum benefits as soon as possible.

[0072] 4) The present invention considers the carbon trading mechanism between microgrids, which is conducive to reducing carbon emissions and making a contribution to green environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the coordinated optimization framework of multi-enterprise microgrids in the present invention;

[0074] Figure 2It is a multi-enterprise microgrid renewable energy power generation prediction diagram in the present invention;

[0075] Figure 3 This is a schematic diagram of the electricity purchase price in the present invention;

[0076] Figure 4 It is a schematic diagram of the dispatching of the microgrid 1 in the present invention;

[0077] Figure 5 It is a schematic diagram of the scheduling of the microgrid 2 in the present invention;

[0078] Figure 6 It is a schematic diagram of the dispatching of the microgrid 3 in the present invention;

[0079] Figure 7 A schematic diagram of power interaction among multiple microgrids in the present invention;

[0080] Figure 8 It is a schematic diagram of the convergence cost curve of multiple microgrids in the present invention;

[0081] Fig. 9 It is a flow chart of the multi-enterprise microgrid energy coordination optimization method in the present invention;

[0082] Fig.10 A schematic diagram of the process of solving the multi-microgrid energy coordination optimization results using the improved Beluga algorithm of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0084] The present invention takes demand response and carbon trading into consideration, proposes a multi-enterprise microgrid energy coordination optimization method, adopts an improved Beluga algorithm to solve the multi-microgrid energy coordination optimization result, introduces a new initialization strategy and reverse learning strategy, improves the convergence performance of the algorithm, and solves the problems of easy falling into local extreme values ​​and low convergence accuracy in the process of solving optimization problems.

[0085] This embodiment relates to a multi-enterprise microgrid energy coordination optimization method, such as Fig. 9 , including the following steps:

[0086] Step S1, establishing a multi-enterprise microgrid coordination optimization framework;

[0087] Step S2: Modeling the internal resources of the enterprise microgrid, including photovoltaic power generation model, wind power generation model, energy storage model, electricity price model, demand response model and carbon trading model;

[0088] Step S3: construct objective functions for single-enterprise microgrid energy optimization and multi-enterprise microgrid energy collaborative optimization; with source-load power balance, equipment output, and interactive power as constraints;

[0089] Step S4, using the improved Beluga optimization algorithm to solve the problem of energy collaborative optimization of multi-enterprise microgrids, and obtaining the optimal scheduling result of the multi-enterprise microgrids;

[0090] Step S5: dispatch the internal resources of the enterprise microgrid according to the optimal dispatch result.

[0091] The multi-enterprise microgrid coordination optimization framework in step S1 is as follows: Figure 1 , where the multi-microgrid includes three microgrid users, microgrid 1 includes wind turbine equipment and energy storage equipment, and microgrids 2 and 3 both include photovoltaic equipment and energy storage equipment. In the power distribution system, each microgrid can be regarded as a user who sells or purchases electricity from other microgrids. Figure 1 In the figure, the dotted line is a two-way communication line, which can communicate and coordinate the control gateway to regulate the distributed power sources and loads. The black line is the power network line. In order to enhance the absorption capacity of new energy in each microgrid, each microgrid will be equipped with a microgrid energy management system (EMS). Assuming that the power purchase cost between microgrids is lower than the public grid electricity price, each microgrid can exchange information and power through the EMS.

[0092] In step S2, the internal resources of the enterprise microgrid are modeled, including photovoltaic power generation model, wind power generation model, energy storage model, electricity price model, demand response model and carbon trading model. The photovoltaic power generation power is positively correlated with the light intensity. When the light intensity increases, the power generation power increases; conversely, when the light intensity decreases, the power generation power also decreases. The expression of the photovoltaic cell output power is as follows:

[0093]

[0094] Where P pv is the active power output by the photovoltaic (PV) cell; R pv is the photovoltaic (PV) output power under standard conditions; q pv is the efficiency coefficient of photovoltaic (PV) power generation; I T I is the irradiance parameter value actually adopted by the small meteorological station in the current area; STC is the radiation intensity parameter value under the standard test environment; α p is the temperature coefficient of the photovoltaic (PV) panel; T cis the photovoltaic (PV) cell temperature under current conditions; T stc is the photovoltaic (PV) cell temperature under standard test conditions.

[0095] The wind power generation model expression is:

[0096]

[0097] Where P WT Represents the output power of the wind turbine; P r Indicates the rated power of the wind turbine; v, v ci 、v r and v co They are actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed, respectively. The cut-in wind speed is the minimum speed for the fan to start, and the cut-out wind speed is the wind speed limit for the fan to prevent damage to the equipment due to excessive wind speed. a, b, c and d are all wind speed parameters.

[0098] The energy storage model expression is:

[0099]

[0100] In the formula, S OC (t) is the remaining power ratio of the energy storage battery (BT) at time t, that is, power at time t / current capacity; P bess (t) is the charging and discharging power (kW) of the energy storage battery (BT), P bess (t) is divided into positive and negative, positive for discharge and negative for charge; Normally, there is an efficiency factor in energy storage charging and discharging, namely, the charging efficiency (η - ) and discharge efficiency (η + ).

[0101] The electricity price model expression is:

[0102]

[0103] Where P G (t) is the power purchase power of the microgrid system at time t, A, B, and C are all power generation cost coefficients; σ is the unit price of electricity, C G (t) is the electricity purchase amount of the microgrid system at time t.

[0104] The demand response model expression is:

[0105] P Le (t) = P Le0 (t)+P cut (t)+P tran (t)

[0106] P Le (t) is the load after demand response at time t; PLe0 (t) is the original load at time t; P cut (t) is the load reduction at time t; P tran (t) is the load transferred at time t. The transferred load satisfies the constraint that the sum of the transferred loads in each period of the day is 0.

[0107] The carbon trading model expression is:

[0108] C co2 =E money ·(E all -H.P buy )

[0109] oeLh all =α·S buy +β·S rew

[0110] C co2 is the carbon trading amount; E all is the total carbon emission quota; H is the carbon emission coefficient; P buy The amount of electricity purchased from the power grid; E money is the unit price of carbon trading (yuan / kg); S buy The number of kWh of electricity purchased from the large power grid; S rew is the electricity generation degree of renewable energy (kWh), α and β are carbon quota coefficients.

[0111] In step 3, the objective function and constraints are constructed, where the energy optimization objective function of a single-enterprise microgrid is:

[0112] min C i =min(C i,storage +C i,demand ,C i,co2 +C i,grid +C i,exc )

[0113] In the formula, C i is the total operating cost of the i-th microgrid; C i,storage is the charging and discharging cost of energy storage; C i,demand is the compensation cost of demand response, which is the sum of the compensation cost of curtailable load and the compensation cost of transferable load; C i,co2 is the carbon trading cost; C i,grid is the transaction cost between the i-th microgrid and the large grid. i,grid When it is positive, it means that the microgrid buys electricity from the large grid; otherwise, it means that the microgrid sells electricity to the large grid. i,exc is the sum of the interaction costs between the i-th microgrid and other microgrids.

[0114] The objective function of multi-enterprise microgrid energy collaborative optimization is:

[0115]

[0116] In the formula, C i,all is the total cost of operating the multi-microgrid system, that is, the sum of the operating costs of multiple microgrids; N is the number of microgrids.

[0117] The power balance expression of source and load in the constraint condition is:

[0118] P i d +P i demand +P i exc =P i rew +P i ESS +P i G

[0119] Among them, P i d is the original load power; P i demand P is the sum of curtailable power and transferable power participating in demand response; i exc The interaction power between the i-th microgrid and other microgrids; P i rew is the power generation of new energy; P i ESS is the charging and discharging power of energy storage; P i G is the interaction power between the i-th microgrid and the large grid;

[0120] The energy storage output and SOC constraints in the constraints are:

[0121]

[0122] In the formula, and They are the upper and lower limits of energy storage charging and discharging power respectively; and They are energy storage SOC i The upper and lower limits of .

[0123] The constraints for interaction power with the large grid and the microgrid are:

[0124]

[0125] and are the interaction power P between microgrid i and the large grid respectively. i G The upper and lower limits of and are the interaction power P between microgrid i and other microgrids respectively. i exc The upper and lower limits of .

[0126] In step 4, the improved Beluga algorithm is used to solve the multi-microgrid energy coordination optimization problem. The principle of the Beluga algorithm is:

[0127] The Beluga Whale Optimization Algorithm (BWO) is an optimization algorithm proposed to simulate the hunting and survival behaviors of beluga whales, which can help solve various optimization problems.

[0128] The essence of BWO is to use white whales as search agents, and each white whale corresponds to a candidate solution. In BWO, the problem of each agent is modeled in the form of a matrix, which covers the position information of each white whale. The code position matrix of BWO is usually a two-dimensional matrix, where each row represents a search agent (white whale) and each column represents a different dimension or feature of the search agent. The position matrix of the search agent can be expressed as:

[0129]

[0130] Where n is the number of beluga whales; d is the dimension. The fitness value F of the beluga whale population x for:

[0131]

[0132] Balance Factor B f The calculation formula is shown below, which is used to determine whether Beluga enters the exploration or development stage.

[0133]

[0134] Where: T represents the current number of iterations; T max is the maximum number of iterations; B 0 is the random number of each generation (0,1); B f The range is between 0 and 1. f When it is less than 0.5, the algorithm enters the development stage; when B f is greater than 0.5, the algorithm enters the exploration phase. As T increases, B f The fluctuation range decreases from (0,1) to (0,0.5), and the probability in the development stage will continue to increase.

[0135] The algorithm enters the exploration phase, and the update position formula of the beluga population is as follows:

[0136]

[0137] Where: T is the current iteration number; is the new position of the i-th beluga whale in the j-th dimension; p j is a random integer selected within the range of dimension j; For the i-th beluga whale at p j The location of the dimension; and are the current positions of the rth beluga and the ith beluga respectively; r is a randomly selected beluga; r 1 and r 2 are two random numbers (0,1), r 1 ∈(0,1); r 2 ∈(0,1).

[0138] BWO introduced the Levy flight strategy during the development phase, which is beneficial to improve the convergence of the algorithm.

[0139] The method is:

[0140]

[0141] Where: T is the current iteration number; is the location of a randomly selected beluga whale; The location of the i-th beluga whale; is the position of the i-th white whale at the T+1th iteration; r 3 and r 3 is a random number between 0 and 1; C 1 Represents the random jump intensity of Levy flight; C 1 The expression is:

[0142] C 1 =2r 4 (1-r / r max )

[0143] L F To meet the random number of Levy flight, the expression is:

[0144]

[0145]

[0146] u and v are random numbers that satisfy the normal distribution; ξ is the exponent of the Levy flight distribution function, and ξ=0.5.

[0147] The BWO algorithm introduces a simulated whale fall behavior to simulate randomness and changes in the beluga group. The calculation formula is as follows:

[0148]

[0149] Where: r 5 、r 6 and r 7 are all random numbers between 0 and 1; X step is the step length of the whale falling, X step The expression is:

[0150] X step =(u b -l b )exp(-C 2 T / T max )

[0151] Where: u b and l b are the upper and lower limits of the variable respectively; T represents the current number of iterations; T max Indicates the maximum number of iterations; C 2 is the population size n and the whale drop probability W f The associated step factor, C 2 The expression is:

[0152] C 2 =2W f ×n

[0153] Where: W f is the probability of a whale falling, W f It is a linear function, which gradually decreases from 0.1 at the beginning to 0.05. Its calculation expression is:

[0154]

[0155] The traditional Beluga algorithm has the problems of being prone to local extrema and slow convergence. The algorithm is improved by adopting a new population initialization strategy and reverse learning strategy. The expression of the new population initialization strategy is:

[0156]

[0157] Where, X T and X T+1 are the individual positions before and after the new type of initialization, respectively.

[0158] The reverse learning strategy expression is:

[0159] X i '=(ub+lb)×θ-X i

[0160] In the formula, ub and lb are the upper and lower limits of the search space, θ is a random number between 0 and 1, and X i Indicates the current individual's position; X i 'Indicates the reverse solution of the current position.

[0161] The process of using the White Whale algorithm to solve the multi-microgrid energy coordination optimization is as follows: Fig.10 ,include:

[0162] 1) Initialize IBWO parameters, including the number of iterations, population size, optimization interval, etc.

[0163] 2) Initialize the population using a new population initialization strategy;

[0164] 3) Calculate the fitness value of individual beluga whales and select the individual with the best fitness;

[0165] 4) Calculate the balance factor B f and whale fall probability W f ;

[0166] 5) If B f If it is less than 0.5, it is in the development stage, otherwise it is in the exploration stage; execute 6);

[0167] 6) If B f Less than W f , it is the whale fall stage, otherwise it is the reverse learning strategy; execute 7);

[0168] 7) Update fitness to determine the best individual;

[0169] 8) If the maximum number of iterations is reached, the multi-microgrid coordinated optimization result is output, otherwise 4) is repeated until the maximum number of iterations is reached.

[0170] This embodiment also relates to an application of a multi-enterprise microgrid energy coordination optimization method. In the embodiment, the time accuracy is 1 hour, and energy scheduling is performed within the next day (00:00-24:00). Figure 1 Three microgrids are used. Microgrid 1 is equipped with wind turbines and energy storage, while microgrids 2 and 3 are equipped with photovoltaics and energy storage. The new energy prediction results of each microgrid are as follows: Figure 2 As shown in the figure, the power purchase price between each microgrid and the large grid is as follows: Figure 3 As shown, the electricity selling price is set at 0.3 yuan / kWh.

[0171] The microgrid parameter settings involved are shown in Table 1.

[0172] Table 1

[0173]

[0174]

[0175] Figure 4 , Figure 5 , Figure 6 These are the output curves of microgrids 1, 2, and 3 in 24 hours. Figure 7 Analysis shows that when the load is low in the early stage from 00:00 to 06:00, more wind power is generated, and part of the excess power is sent to the power grid, and part of the power is exchanged with other parks or charged into energy storage equipment. When the load increases from 07:00 to 20:00, wind power cannot meet the needs of the park. At this time, electricity can be purchased from other parks, and energy storage can also be discharged to seek maximum benefits; when the load demand decreases from 21:00 to 23:00, wind power generation is high, and excess electricity is stored in energy storage and sold to other parks. Figure 4 and Figure 6 The same principle applies to the analysis of photovoltaic parks.

[0176] Figure 7 is the interaction power between microgrid groups, the inflow of electric energy is positive, and the outflow is negative; Figure 8 is the cost convergence curve of the microgrid group, Figure 8 It can be seen that the three curves finally converged from negative to positive, indicating that the microgrid group began to benefit. This shows the effectiveness of the proposed algorithm in the energy coordination optimization of the microgrid group.

[0177] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0178] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0179] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method in any other appropriate manner (e.g., by means of firmware).

[0180] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0181] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0182] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0183] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multi-enterprise microgrid energy coordination optimization method, characterized in that: The method comprises the following steps: Step S1: Establish a multi-enterprise microgrid coordination optimization framework, and exchange information and power between microgrids; Step S2: Modeling the internal resources of the enterprise microgrid, including photovoltaic power generation model, wind power generation model, energy storage model, electricity price model, demand response model and carbon trading model; Step S3: Based on the multi-enterprise microgrid coordinated optimization framework in step S1 and the model in step S2, objective functions and constraints for single-enterprise microgrid energy optimization and multi-enterprise microgrid energy collaborative optimization are constructed respectively; Step S4: using the improved Beluga optimization algorithm to solve the problem of multi-enterprise microgrid energy collaborative optimization, and outputting the optimal dispatching result of the multi-enterprise microgrid; Step S5: dispatch the internal resources of each enterprise microgrid according to the optimal dispatch result.

2. A multi-enterprise microgrid energy coordination optimization method according to claim 1, characterized in that: In the multi-enterprise microgrid coordinated optimization framework, each microgrid is regarded as a user that sells or purchases electricity from other microgrids.

3. A multi-enterprise microgrid energy coordination optimization method according to claim 1, characterized in that: The photovoltaic power generation model is specifically: Where P pv is the active power output of the photovoltaic cell; R pv is the photovoltaic output power under standard conditions; q pv is the efficiency coefficient of photovoltaic power generation; I T I is the irradiance parameter value actually adopted by the small meteorological station in the current area; STC is the radiation intensity parameter value under the standard test environment; α p is the temperature coefficient of the photovoltaic panel; T c is the photovoltaic cell temperature under current conditions; T stc is the photovoltaic cell temperature under standard test environment; The wind power generation model is specifically as follows: Where P WT Represents the output power of the wind turbine; P r Indicates the rated power of the wind turbine; v, v ci 、v r and v co are actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed respectively; a, b, c and d are wind speed parameters; The energy storage model is specifically: In the formula, S OC (t) is the remaining power ratio of the energy storage battery at time t; P bess (t) is the charging and discharging power of the energy storage battery, P bess (t) is discharged when positive and charged when negative; η - is the charging efficiency, η + is the discharge efficiency.

4. The multi-enterprise microgrid energy coordination optimization method according to claim 1 is characterized in that: The electricity price model is specifically as follows: In the formula, C G (t) is the power purchase amount of the microgrid system at time t, P G (t) is the power purchase power of the microgrid system at time t, A, B, and C are all power generation cost coefficients, and σ is the unit price of electricity; The demand response model is specifically as follows: P Le (t)=P Le0 (t)+P cut (t)+P tran (t) Where P Le (t) is the load after demand response at time t, P Le0 (t) is the original load at time t, P cut (t) is the load reduction at time t, P tran (t) is the load transferred at time t; The carbon trading model is specifically as follows: C co2 =E money ·(E all -H·P buy ) stE all =α·S buy +β·S rew In the formula, C co2 is the carbon trading amount, E all is the total carbon emission quota, H is the carbon emission coefficient, P buy is the amount of electricity purchased from the power grid, E money is the unit price of carbon trading, S buy To purchase electricity from the large power grid, S rew is the electricity generation degree of renewable energy, α and β are the carbon quota coefficients.

5. The multi-enterprise microgrid energy coordination optimization method according to claim 1 is characterized in that: The objective function of the energy optimization of the single-enterprise microgrid is specifically: minC i =min(C i,storage +C i,demand ,C i,co2 +C i,grid +C i,exc ) In the formula, C i is the total operating cost of the i-th microgrid; C i,storage is the charging and discharging cost of energy storage; C i,demand is the compensation cost of demand response, which is the sum of the compensation cost of load reduction and the compensation cost of load transferability; C i,co2 is the carbon trading cost; C i,grid is the transaction cost between the i-th microgrid and the large grid. i,grid When it is positive, it means that the microgrid buys electricity from the large grid, otherwise, it means that the microgrid sells electricity to the large grid; C i,exc is the sum of the interaction costs between the i-th microgrid and other microgrids; The objective function of the multi-enterprise microgrid energy collaborative optimization is specifically: In the formula, C i,all is the total cost of operating the multi-microgrid system, that is, the sum of the operating costs of multiple microgrids; N is the number of microgrids.

6. A multi-enterprise microgrid energy coordination optimization method according to claim 1, characterized in that: The constraints include source-load power balance constraints, energy storage output constraints, SOC constraints and interactive power constraints; The source-load power balance constraint is specifically: P i d +P i demand +P i exc =P i rew +P i ESS +P i G Among them, P i d is the original load power; P i demand P is the sum of curtailable power and transferable power participating in demand response; i exc The interaction power between the i-th microgrid and other microgrids; P i rew is the power generation of new energy; P i ESS is the charging and discharging power of energy storage; P i G is the interaction power between the i-th microgrid and the large grid; The energy storage output constraint and SOC constraint are: In the formula, is the energy storage charging and discharging power P i ESS The lower limit of is the energy storage charging and discharging power P i ESS The upper limit of For energy storage SOC i The lower limit of For energy storage SOC i upper limit.

7. A multi-enterprise microgrid energy coordination optimization method according to claim 6, characterized in that: The interactive power constraints include the interactive power constraints between the microgrid and the large grid, and the interactive power constraints between microgrids, specifically: In the formula, and are the interaction power P between microgrid i and the large grid respectively. i G The upper and lower limits of and are the interaction power P between microgrid i and other microgrids respectively. i exc upper and lower limits.

8. The multi-enterprise microgrid energy coordination optimization method according to claim 1 is characterized in that: The White Whale optimization algorithm is improved by adopting a new population initialization strategy and a reverse learning strategy. The new population initialization strategy is specifically as follows: Where, X T and X T+1 are the individual positions before and after the novel initialization, respectively; The reverse learning strategy is specifically: X i '=(ub+lb)×θ-X i In the formula, ub and lb are the upper and lower limits of the search space, θ is a random number between 0 and 1, and X i Indicates the current individual's position; X i 'Indicates the reverse solution of the current position.

9. A multi-enterprise microgrid energy coordination optimization method according to claim 8, characterized in that: The process of step S4 includes: Step S41, initializing parameters; Step S42, initializing the population using a new population initialization strategy; Step S43, calculating the fitness values ​​of individual beluga whales and selecting the individual with the best fitness; Step S44, calculate the balance factor B f and whale fall probability W f ; Step S45, if B f If it is less than 0.5, it is the development stage, otherwise it is the exploration stage; execute step S46; Step S46, if B f Less than W f , then it is the whale fall stage, otherwise it is the reverse learning strategy; execute step S47; Step S47, updating the fitness to determine the best individual; Step S48: if the maximum number of iterations is reached, the multi-microgrid coordinated optimization result is output; otherwise, the process returns to step S44 until the maximum number of iterations is reached.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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