Optimized dispatching method, model and equipment for wind-solar-stored-diesel micro-grid based on improved MGO algorithm, and storage medium

Abstract

The invention discloses an improved MGO algorithm-based wind-solar-stored-diesel micro-grid optimal scheduling method, model and device, and a storage medium, and relates to the technical field of energy optimal scheduling, and the method comprises the steps: initializing population parameters and population individuals, and calculating the fitness of the initial population individuals; constructing an evolution direction vector based on a distance relationship between population individuals; judging a wind state according to the wind intensity coefficient, and correspondingly selecting a diffusion search strategy according to the wind state to update the individual positions of the population; based on an evolution direction vector, fusing a differential variation search strategy and a dual-propagation search strategy, introducing a multi-path search mechanism based on random number driving, and improving a diffusion search strategy; and if the population individual fitness is not improved after the diffusion search strategy is improved, replacing by using an optimal record in historical iteration until an iteration termination condition is met, and outputting a population individual optimal solution. According to the method, the high efficiency and the reliability of the optimal dispatching result of the wind-solar-stored-diesel micro-grid are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy optimization scheduling, in particular to a wind-solar-storage-diesel microgrid optimization scheduling method, model, device and storage medium based on an improved MGO algorithm. BACKGROUND

[0002] With the acceleration of energy transformation and the wide access of distributed energy, microgrids integrated with renewable energy (such as wind power and photovoltaic), energy storage systems (such as batteries) and traditional diesel generators have become an important solution for power supply in remote areas, important load protection and improvement of energy utilization efficiency due to their flexibility, reliability and environmental potential. However, the efficient and stable operation of wind-solar-storage-diesel microgrids faces significant challenges: wind and solar power generation has significant intermittency and randomness, load demand fluctuates constantly, energy storage systems need to be reasonably charged and discharged to smooth fluctuations and realize arbitrage, and diesel generators need to minimize their high operating costs and carbon emissions while ensuring power supply reliability. This makes the optimization scheduling of microgrids a complex multi-objective (economy, environmental protection, reliability), multi-constrained (power balance, device operation constraints), nonlinear, high-dimensional optimization problem.

[0003] Existing scheduling optimization mostly uses particle swarm optimization (PSO), genetic algorithm (GA) and the like, which has obvious limitations: PSO is prone to local optimum and difficult to balance wind and solar randomness and diesel power generation cost; GA has slow convergence speed and insufficient ability to optimize the energy consumption of newly added devices such as cooling systems; traditional MGO algorithm has global search foundation but lacks targeted local development mechanism, and has limited precision in multi-device collaborative scenarios. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing microgrid scheduling optimization method is prone to local optimum when dealing with the intermittency and randomness of wind and solar power generation, has slow convergence speed, insufficient processing capacity for energy storage device charging and discharging management and diesel engine operation constraints, and lacks a collaborative optimization mechanism for cooling system energy consumption; how to realize efficient and stable operation and low-cost scheduling of wind-solar-storage-diesel microgrids under multi-objective and multi-constrained conditions has become a technical problem to be solved.

[0006] To solve the above technical problems, the application provides the following technical scheme: a wind-solar-storage-chip micro-grid optimization scheduling method based on an improved MGO algorithm, which comprises initializing population parameters and population individuals, and calculating initial population individual fitness; an evolutionary direction vector is constructed based on the distance relationship between population individuals; the wind state is judged according to the wind intensity coefficient, and the population individual position is updated according to the corresponding selection of the diffusion search strategy according to the wind state; based on the evolutionary direction vector, the diffusion search strategy is improved by introducing a multi-path search mechanism driven by random numbers based on the fusion of the differential mutation search strategy and the double reproduction search strategy; if the population individual fitness is not improved after the diffusion search strategy is improved, the optimal record in the history iteration is used for replacement until the iteration termination condition is met, and the population individual optimal solution is output.

[0007] As a preferred scheme of the wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm, the initialization of population parameters and population individuals comprises initializing the population size, the maximum iteration number, the wind field division dimension number, the spore diffusion step length coefficient and the cryptic record threshold value, randomly generating the initial population individuals, calculating the initial population individual fitness and recording the global optimal population individual.

[0008] As a preferred scheme of the wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm, the evolutionary direction vector is constructed based on the distance relationship between population individuals, which comprises dividing the population into a plurality of population individual sets and a small number of population individual sets through dimension division, taking the distance mean of the plurality of population individuals and the global optimal population individual as the evolutionary direction vector, and representing it as: , , wherein, is the distance between the plurality of population individuals and the global optimal population individual, is the global optimal population individual, is the mth individual in the plurality of population individuals, is the plurality of population individuals, is the evolutionary direction vector, is the distance between the individual and the global optimal population individual.

[0009] As a preferred scheme of the wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm, the population individual position is updated according to the corresponding selection of the diffusion search strategy according to the wind state, which comprises judging the wind state as stable wind and turbulent wind according to the wind intensity coefficient, and updating the population individual position by using the long-step global exploration strategy when the wind state is judged as stable wind. ​​​

[0010] When the wind is judged to be turbulent flow, a short step local search strategy is used to update the population individual position.

[0011] As a preferred scheme of the wind and light storage and diesel micro-grid optimization scheduling method based on the improved MGO algorithm, wherein: the improvement of the diffusion search strategy includes generating a random number for each population individual , When , the improved spore diffusion search is used to update the population individual position, and the mutation operator of the differential evolution is combined to expand the search range, which is represented as: , Wherein, is the position of the updated population individual, is the position of the current population individual, is the adaptive step size, is the mutation factor, , is a randomly selected population individual. When

[0012] , the double reproduction search strategy is used, 60% probability is executed based on sexual reproduction local development, and the secondary interpolation strategy is fused to optimize the population individual position, 40% probability is executed based on vegetative reproduction neighborhood search, and the simulated annealing acceptance probability is introduced to jump out of the local optimum. As a preferred scheme of the wind and light storage and diesel micro-grid optimization scheduling method based on the improved MGO algorithm, wherein: the use of the optimal record in the history iteration to replace includes, after the improvement of the diffusion search strategy, the position of the population individual in each iteration is recorded through the improved cryptobiotic mechanism, if the population individual is continuously iterated for a preset number of times, and the fitness of the population individual is not improved, the history global optimal population individual 101 is replaced by the current population individual, and the cryptobiotic record counter is reset.

[0013] As a preferred scheme of the wind and light storage and diesel micro-grid optimization scheduling method based on the improved MGO algorithm, wherein: the output of the optimal solution of the population individual includes, according to the day-ahead optimization model and the position of the updated population individual, the fitness of each population individual is calculated, the position of the population individual with the lowest fitness and the fitness of the population individual are updated, and the iteration number is updated. When the iteration number reaches the maximum iteration number, the position of the population individual with the lowest fitness in the history iteration is output as the day-ahead optimization scheduling strategy.

[0014]

[0015] ​Another object of the present application is to provide a wind-solar-storage-chip micro-grid optimization scheduling model based on an improved MGO algorithm, which can solve the problems of slow convergence speed, easy to fall into local optimum and lack of collaborative optimization ability of multi-source equipment in the prior art by constructing a day-ahead optimization model, solving the day-ahead optimization model through the improved MGO algorithm and obtaining a day-ahead optimization scheduling strategy.

[0016] As a preferred scheme of the wind-solar-storage-chip micro-grid optimization scheduling model based on the improved MGO algorithm, the day-ahead optimization model is established according to various types of distributed energy in the micro-grid, and the day-ahead optimization model is solved through the improved MGO algorithm to obtain the day-ahead optimization scheduling strategy.

[0017] Another object of the present application is to provide a wind-solar-storage-chip micro-grid optimization scheduling device based on an improved MGO algorithm, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the steps of the computer program to implement the wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm.

[0018] Another object of the present application is to provide a wind-solar-storage-chip micro-grid optimization scheduling storage medium based on an improved MGO algorithm, which stores a computer program, and the computer program is executed by a processor to implement the steps of the wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm.

[0019] The wind-solar-storage-chip micro-grid optimization scheduling method based on the improved MGO algorithm provided by the present application sets population parameters and calculates fitness in the initialization stage, ensures the diversity and stability of the search, constructs a direction vector by using the distance between individuals in the evolution process, improves the convergence direction, determines the wind state by introducing the wind intensity coefficient and selecting the corresponding diffusion search strategy, realizes the dynamic balance of global exploration and local development, further combines the differential mutation and double breeding strategy, combines the multi-path mechanism driven by random numbers, enhances the ability to jump out of the local optimum and maintain the diversity of the solution set, and at the same time, cooperates with the replacement mechanism of the historical optimal solution, improves the robustness and global optimal solution acquisition ability of the algorithm, so as to ensure the efficiency and reliability of the wind-solar-storage-chip micro-grid optimization scheduling result. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0021] Figure 1A wind-solar-storage-chip micro-grid optimization scheduling method based on an improved MGO algorithm is provided for the embodiment 1 of the present application.

[0022] Figure 2 A wind-solar-storage-chip micro-grid system structure and energy flow direction schematic diagram of a wind-solar-storage-chip micro-grid optimization scheduling model based on an improved MGO algorithm is provided for the embodiment 3 of the present application. DETAILED DESCRIPTION

[0023] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0024] Embodiment 1, refer to Figure 1 For an embodiment of the present application, a wind-solar-storage-chip micro-grid optimization scheduling method based on an improved MGO algorithm is provided, comprising: S1: initializing population parameters 100 and population individuals, and calculating initial population individual fitness.

[0025] Further, initializing population parameters 100 and population individuals includes initializing population size, maximum iteration number, wind field division dimension number, spore diffusion step length coefficient, cryptic record threshold, randomly generating initial population individuals, calculating initial population individual fitness and recording global optimal population individual 101.

[0026] It should be noted that initializing population parameters 100 includes initializing population size, maximum iteration number, wind field division dimension number, spore diffusion step length coefficient, cryptic record threshold, randomly generating initial moss individuals, each individual corresponding to a set of scheduling strategies, calculating initial fitness and recording global optimal population individual 101.

[0027] It should also be noted that by setting population size, wind field division dimension, spore diffusion step length coefficient in the initial stage, and introducing a cryptic record threshold, the diversity of the population in global search is ensured, and a history high-quality solution replacement mechanism is provided for the subsequent, thereby improving the stability and robustness of the algorithm.

[0028] S2: constructing an evolution direction vector 200 based on the distance relationship between population individuals.

[0029] Further, constructing the evolution direction vector 200 based on the distance relationship between population individuals includes dividing the population into a majority population individual set and a minority population individual set through dimension division, taking the distance mean of the majority population individuals and the global optimal population individual 101 as the evolution direction vector, denoted as: , , wherein, is the distance of the majority population individuals and the global optimal population individual 101, is the global optimal population individual 101, is the i-th individual in the majority population, is the majority population individual, is the evolution direction vector, is the distance between the individual and the global optimal population individual 101.

[0030] It should be noted that the population is dimensionally divided, and each population individual in the majority population individual set and the current global optimal population individual 101 are divided into a majority population individual set and a minority population individual set, and the distance between each population individual in the majority population individual set and the current global optimal population individual 101 is taken as a measure, the mean is calculated, and the mean is taken as the evolution direction vector.

[0031] It should also be noted that by calculating the distance relationship between the majority individuals and the global optimal individual and taking the mean as the evolution direction vector, the overfitting phenomenon caused by the guidance of a single optimal individual can be avoided, and the population evolution is more diverse and directional. Through the above improvement, the evolution direction is no longer dependent on individual solutions, but combines the information of the majority individuals to determine the evolution direction, so that the algorithm can obtain higher convergence speed and stronger global optimization ability in the scheduling solution of the wind-solar-biomass micro-grid.

[0032] S3: judging the wind state according to the wind intensity coefficient, and updating the population individual position according to the corresponding diffusion search strategy according to the wind state.

[0033] Further, updating the population individual position according to the corresponding diffusion search strategy according to the wind state includes judging the wind state to be stable wind and turbulent wind 300 according to the wind intensity coefficient, and updating the population individual position 301 using a long-step global exploration strategy when the wind state is judged to be stable wind.

[0034] When the wind state is judged to be turbulent wind, a short-step local search strategy is used to update the population individual position 302.

[0035] It should be noted that the wind intensity coefficient is calculated as: ,​​ in, Wind intensity coefficient This represents the current iteration number. This represents the maximum number of iterations.

[0036] It should also be noted that by introducing a wind intensity coefficient to determine the wind state and distinguishing between steady wind and turbulent wind, a long-step global search or a short-step local search can be adopted for different states, realizing adaptive switching of the search range and improving the balance between global exploration and local development. The wind intensity coefficient gradually decreases as the number of iterations increases, so that the search process gradually transitions from a large-scale global exploration to a local fine search, improving convergence efficiency and solution accuracy.

[0037] S4: Based on the evolutionary direction vector, the differential mutation search strategy and the dual reproduction search strategy are integrated 402, and a multi-path search mechanism driven by random numbers is introduced to improve the diffusion search strategy 400.

[0038] Furthermore, improvements to the diffusion search strategy 400 include generating random numbers for each individual in the population. , ,when At time 400', the improved spore dispersal search 401 is used to update the individual positions in the population. The search range is expanded by combining the mutation operator of differential evolution, as shown below: , in, For the updated number The location of an individual in a population This represents the current position of an individual in the population. For adaptive step size, As a variable factor, , Individuals in the population are randomly selected.

[0039] when At time 400'', a dual-reproduction search strategy 402 is adopted, with a 60% probability of performing local development based on sexual reproduction, and a quadratic interpolation strategy is integrated to optimize the position of individuals in the population. A 40% probability of performing neighborhood search based on vegetative reproduction is also adopted, and simulated annealing is introduced to accept the probability of escaping local optima.

[0040] It should be noted that the calculation of spore diffusion step length under steady wind conditions... Represented as: , in, To stabilize the spore dispersal step length under wind, This is the step size coefficient. It is a random vector, and .

[0041] Calculate the spore diffusion step size under turbulent wind Represented as: , in, The spore diffusion step length under turbulent wind. It is a random vector, and , The majority of individuals account for the majority.

[0042] The formula for sexual reproduction and renewal is expressed as: , in, For newly generated individuals in the population, The gene fusion coefficient. This is the optimal individual in the population.

[0043] The nutrient reproduction renewal formula is expressed as: , in, For newly generated population individuals in the first dimensional components, For the optimal population individual in the th dimensional components, This is the local search step size. The evolution direction vector is the first Dimensional components.

[0044] It should also be noted that by using different step length calculation methods under stable and turbulent winds, combined with the dynamic changes in wind intensity coefficient, an adaptive adjustment of the step length is achieved, which gradually shrinks as the iteration process progresses. This allows the search to gradually transition from global expansion to local convergence. By combining the mutation operator of differential evolution with the evolution direction vector, the search range is expanded, enabling individuals to maintain exploration diversity under the guidance of global optimality and effectively avoiding premature convergence. Under random number triggering conditions, a dual reproduction strategy is executed. The sexual reproduction formula uses gene fusion to make new individuals closer to the optimal individual, while the vegetative reproduction formula searches in the neighborhood of the optimal solution based on the wind vector and the local step length, thereby improving the accuracy of local development.

[0045] S5: If the fitness of individuals in the population does not improve after the diffusion search strategy is improved, replace 500 with the best record in the historical iteration until the iteration termination condition is met, and output the best solution for individuals in the population, 600.

[0046] Further, the replacing 500 using the optimal record in the history iteration includes, when the diffusion search strategy is improved, recording the position of the population individual in each iteration by improving the hidden mechanism, if the fitness of the population individual is not improved after the population individual is iterated for a preset number of times, waking up the historical global optimal population individual 101 to replace the current population individual, and resetting the hidden record counter.

[0047] It should be noted that when the diffusion search strategy is improved, the position of the population individual in each iteration is recorded by improving the hidden mechanism; if the fitness of a certain type of population individual is not improved for 3 consecutive iterations, the historical global optimal population individual 101 is awakened to replace it to ensure that the search process does not stagnate. At the same time, after the replacement operation is completed, the hidden record counter is reset to continue recording the new iteration process, ensuring that the algorithm can dynamically maintain population diversity and enhance global search capability.

[0048] It should also be noted that the position of the population individual in each iteration is recorded by improving the hidden mechanism, and the historical global optimal individual is awakened to replace the current individual when the fitness of the individual is not improved for 3 consecutive iterations, effectively preventing the algorithm from stagnating and ensuring that the search process continues. After the replacement operation is completed, the hidden record counter is reset so that it can continue to track the new evolution process, thereby ensuring the dynamic adaptive ability of the algorithm in the global range.

[0049] Further, the outputting 600 of the optimal solution of the population individual includes calculating the fitness of each population individual according to the day-ahead optimization model and the position of the updated population individual, recording the position of the population individual with the lowest fitness and the fitness of the population individual, and updating the iteration number. When the iteration number reaches the maximum iteration number 601, the position of the population individual with the lowest fitness in the history iteration is output as the day-ahead optimization scheduling strategy.

[0050] It should be noted that according to the position of the updated population individual, the fitness of each individual is calculated, the position of the individual with the lowest fitness and its fitness are recorded, and the iteration number is updated. When the iteration number has not reached the maximum iteration number, the wind direction mechanism and the search strategy step are returned to continue optimization; when the iteration number reaches the maximum iteration number, the position of the population individual with the lowest fitness in the history iteration is output.

[0051] It should also be noted that by calculating and updating the fitness of all population individuals in each iteration, the algorithm can reflect the constraint conditions and optimization objectives of the pre-optimization model in real time during the optimization process, thereby improving the reliability of the solution. When the iteration number reaches the maximum set value, the position of the individual with the lowest fitness in the history iteration is directly output as the final scheduling result, ensuring that the optimization process converges to a global optimal or approximate global optimal solution, and avoiding infinite loops in the calculation process.

[0052] Embodiment 2 provides a wind-solar-storage-chip micro-grid optimal scheduling method based on an improved MGO algorithm, which is an embodiment of the present application. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are carried out for scientific demonstration.

[0053] A certain island micro-grid supplies power to residents and small-scale industry and commerce, adopts a "wind-solar-storage-chip" hybrid energy structure, and is equipped with cooling equipment to ensure safe operation. The "economic + environmental + reliability" triple goals need to be achieved in the independent operation mode, the scheduling period is 1 day (24 hours), the scheduling data is collected every 15 minutes, the scheduling strategy is generated every half hour (rolling optimization period 0.5 hours), and the power generation equipment of the small micro-grid is shown in Table 1.

[0054] Table 1 Power generation equipment table of small micro-grid

[0055] The environmental and load data of the small micro-grid are shown in Table 2.

[0056] Table 2 Environmental and load data table of small micro-grid

[0057] Among them, the environmental data such as light, wind speed, ambient temperature and the like are directly obtained from a specific website; the user load is predicted according to the load of the previous day, and the predicted data are used as the data source for optimal scheduling analysis.

[0058] The economic and environmental parameters are configured, the diesel price is set to 8.5 yuan / L, the maintenance cost is set to 0.02 yuan / kWh for photovoltaic, 0.03 yuan / kWh for wind power, 0.01 yuan / kWh for energy storage, and 0.05 yuan / kWh for DG; the pollutant treatment cost is 0.01 yuan / kg for CO2 and 0.5 yuan / kg for NO x 0.8 yuan / kWh for cooling.

[0059] The improved MGO algorithm parameter setting is shown in Table 3.

[0060] Table 3 Improved MGO algorithm parameter setting table

[0061] The target function optimization result is shown in Table 4.

[0062] Table 4 Target function optimization result table Cost type Improved MGO (yuan / day) Schedule (yuan / day) Optimization range Operating cost 338.3 759.2 55.4% Emission cost 65.8 89.2 26.2% Cooling cost 52.3 76.8 32.0% Light waste cost 0 28.5 100% Total target cost 456.4 953.7 47.7%

[0063] From the operation data, compared with the planned scheduling, the wind-solar-storage-diesel scheduling strategy based on the improved MGO algorithm reduces the start of the diesel generator several times a day. Through the optimal solution of the objective function, the economic scheme of the system is found by adjusting the wind, light and storage power every half hour, and the flexibility and robustness of the system operation are improved.

[0064] Embodiment 3, refer to Figure 2 For an embodiment of the present application, a wind-solar-storage-diesel microgrid optimization scheduling model based on an improved MGO algorithm is provided, which comprises establishing a day-ahead optimization model according to various types of distributed energy in the microgrid, solving the day-ahead optimization model by the improved MGO algorithm, and obtaining a day-ahead optimization scheduling strategy.

[0065] Specifically, as shown in Figure 2 The diesel generator, wind turbine, photovoltaic system, energy storage system and load are the core components of the microgrid.

[0066] In detail, the diesel generator 700 is the only active power generation equipment that can be stably regulated in the microgrid, and its core role is to balance the supply and demand and to adjust the controllable cost. When the wind and photovoltaic output is insufficient or the load suddenly increases, the output can be flexibly adjusted to make up for the power gap and avoid the penalty cost caused by the lack of load. The fuel cost and pollutant emission cost generated by its operation are the key components of the comprehensive cost, and the operating temperature of the diesel generator itself will affect the energy consumption of the cooling system. The improved MGO algorithm optimizes the start-stop state and output size of the diesel generator to minimize these two core costs while ensuring power supply stability.

[0067] The wind turbine and photovoltaic system 701 are the clean and renewable energy sources of the microgrid, and their core role is to provide low-cost electricity and reduce dependence on fossil fuels, and to preferentially consume clean electricity to reduce the comprehensive cost. However, the output of the wind turbine and photovoltaic system is intermittent and fluctuates due to natural conditions, which poses a challenge to scheduling. However, the strong global optimization ability of the improved MGO algorithm (such as the hidden mechanism and adaptive search) can accurately match the fluctuation characteristics of the wind turbine and photovoltaic system, maximize the consumption of clean electricity, and reduce the dependence on diesel generators. At the same time, the temperature of the wind turbine and photovoltaic system needs to be maintained at a normal operating temperature through the cooling system, and the algorithm will balance the renewable energy consumption and cooling energy consumption at the same time.

[0068] The core role of the energy storage system 702 is to smooth fluctuations and improve scheduling flexibility. When the wind and photovoltaic output is excessive, the energy storage system stores electricity; when the output is insufficient or the load is high, the energy storage system discharges to supplement power supply, which can not only reduce the loss of abandoned wind and light, but also reduce the additional cost of frequent start-stop of diesel generators. The charging and discharging state of the energy storage system needs to meet the safety constraints, and the improved MGO algorithm takes the charging and discharging strategy of the energy storage system as the core optimization variable to achieve smooth matching of power supply and load through dynamic adjustment, thereby enhancing the stability of system operation.

[0069] Load 703, as the electricity consumer in the microgrid, plays a crucial role in defining dispatch objectives and triggering constraints and costs. The dispatch of all power sources is predicated on meeting load demand, serving as the core benchmark for power balance constraints. If the total output of the power sources cannot match the load demand, a load deficit penalty cost will be incurred, directly incorporated into the overall cost target. Simultaneously, intraday load fluctuations (such as peak-valley differences) require the dispatch strategy to possess dynamic response capabilities. Improving the fast convergence characteristics of the MGO algorithm allows for accurate tracking of load changes, outputting an adaptive real-time dispatch scheme.

[0070] The day-ahead optimization model includes obtaining a mathematical model of the distributed power sources of the microgrid in the area to be optimized, constructing an objective function with the weighted minimization of the microgrid's fuel cost, operation and maintenance cost, pollutant emission treatment cost and load control compensation cost as the objective, and setting power balance constraints, energy storage state of charge constraints, power output constraints of each power source and diesel generator ramp rate constraints to construct the day-ahead optimization model.

[0071] Distributed power sources in a microgrid include wind turbines, solar photovoltaic cells, and energy storage batteries. The segmented wind speed-power curve mathematical model of a wind turbine is expressed as follows: , in, This represents the functional relationship between the output power of a wind turbine and wind speed. Wind speed (m / s) To cut into wind speed, Rated wind speed, To cut off the wind speed, Rated power (kW)

[0072] The correction factor for air density variations with altitude, air pressure, and temperature is expressed as follows: , in, In order to be at a high altitude and temperature Atmospheric density under certain conditions The standard density is 1.225 kg / m³. Actual atmospheric pressure (Pa), Actual temperature (K), These are standard atmospheric pressure and standard temperature, respectively.

[0073] The power output model of a solar photovoltaic cell is expressed as follows: , in, In time At any given moment, the output power of solar photovoltaic cells ηpv is the photovoltaic inverter efficiency (0.95-0.98), I is the horizontal plane solar radiation intensity (W / m²), A is the total photovoltaic area (m²), ηstd is the standard photovoltaic module efficiency, α is the temperature coefficient (1 / K), Tpv is the photovoltaic module temperature, Tref is the reference temperature (25 ).

[0074] The photovoltaic module temperature is expressed as: , where, Tamb is the ambient temperature (°C), Tstd is the standard photovoltaic module operating temperature. The fuel consumption rate of the diesel generator is expressed as a quadratic model:

[0075] , where, ηf is the fuel consumption rate of the diesel generator at time t, η0 is the no-load fuel consumption (kg / h), a is the first order fuel consumption coefficient (kg / kWh), b is the second order fuel consumption coefficient (kg / kW²h), P is the unit power (kW). The energy conversion plant comprises a natural ventilation cooling plant and a liquid cooling plant.

[0076] The energy consumption model of the natural ventilation cooling plant is expressed as:

[0077] , where, ηc is the natural ventilation cooling system energy consumption at time t, T is the plant temperature at time t, Tset is the set temperature threshold, α is the non-linear temperature increase region parameter, β is the linear temperature increase region parameter, η0 is the base energy consumption parameter. The energy consumption model of the liquid cooling plant is expressed as: ,

[0078] , , where, ηc is the liquid cooling system energy consumption at time t, T is the plant temperature at time t, Tset is the set temperature threshold, η0 is the base energy consumption parameter. P total energy consumption of liquid cooling equipment, Ppump Pchiller Q H p g et COP Q , C ,

[0079] Objective function is expressed as: , wherein, F TC CE PC CC , =1).

[0080] Power balance constraint is expressed as: , wherein, P Pd , , Ppv, Pw, Pd respectively represent photovoltaic, wind power and diesel generator output, , Pbatt Pgrid

[0081] Natural cooling equipment operation constraints include: , , wherein, Pmax , Tmin, Tmax represent equipment allowed operation temperature range.

[0082] Energy storage state of charge constraints include: , , wherein, SOCmin, SOCmax state of charge at time t, , respectively the charge and discharge power, , the charge and discharge efficiency, the rated capacity.

[0083] The diesel generator ramp rate constraint is expressed as: , wherein, is the output power at time t, , the maximum up / down ramp rate.

[0084] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatus, or devices.

[0086] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory in a suitable manner.

[0087] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art of hardware implementation, can be used: a combination of logic gates in a logic circuit, a combination of processor(s) and memory that forms a special purpose machine and has appropriate software, a combination of application specific integrated circuits (ASICs), a combination of one or more state machines with memory (for use as a processor), etc.

[0088] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, since the technical solutions of the application can be modified in various ways without departing from the spirit and scope thereof. Accordingly, the scope of the application is to be construed in accordance with the substance of the appended claims rather than the examples described herein.

Claims

1. A method for optimal scheduling of wind-solar-storage-diesel microgrids based on an improved MGO algorithm, characterized in that, include: Initialize the population parameters and individuals, and calculate the fitness of the initial population individuals; Construct an evolutionary direction vector based on the distance relationships between individuals in the population; The wind state is determined based on the wind intensity coefficient, and the diffusion search strategy is selected accordingly to update the location of individuals in the population. Based on the evolutionary direction vector, the differential mutation search strategy and the dual reproduction search strategy are integrated, and a multi-path search mechanism driven by random numbers is introduced to improve the diffusion search strategy; If the fitness of individuals in the population does not improve after the diffusion search strategy is improved, the best record in the historical iteration is used for replacement until the iteration termination condition is met, and the optimal solution for individuals in the population is output.

2. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in claim 1, characterized in that: The initialization of population parameters and population individuals includes initializing population size, maximum number of iterations, number of wind field division dimensions, spore diffusion step size coefficient, and cryptogenic record threshold; randomly generating initial population individuals; calculating the fitness of initial population individuals; and recording the globally optimal population individual.

3. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in claim 2, characterized in that: The construction of the evolutionary direction vector based on the distance relationship between individuals in the population includes dividing the population into a majority population set and a minority population set by dimensional partitioning, and using the average distance between the majority population individuals and the globally optimal population individual as the evolutionary direction vector, expressed as: ， ， in, This represents the distance between the majority of individuals in the population and the globally optimal individual. It is the globally optimal individual in the population. The first in most populations Individual, For the majority of individuals in the population, This is the evolutionary direction vector. For individuals With the globally optimal population individual The distance between them.

4. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in claim 3, characterized in that: The step of updating the position of individuals in the population by selecting a diffusion search strategy according to the wind state includes determining whether the wind state is stable wind or turbulent wind based on the wind intensity coefficient. When the wind state is determined to be stable wind, a long step global exploration strategy is used to update the position of individuals in the population. When the wind is identified as turbulent, a short-step local search strategy is used to update the positions of individuals in the population.

5. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in any one of claims 2, 3, and 4, characterized in that: The improvement to the diffusion search strategy includes generating random numbers for each individual in the population. , ,when At that time, an improved spore dispersal search is used to update the individual positions in the population, and the search range is expanded by combining the mutation operator of differential evolution, as expressed as: ， in, For the updated number The location of an individual in a population This represents the current position of an individual in the population. For adaptive step size, As a variable factor, , Individuals in the population are randomly selected; when At that time, a dual-reproduction search strategy is adopted, with a 60% probability of performing local development based on sexual reproduction, and a quadratic interpolation strategy is integrated to optimize the position of individuals in the population, and a 40% probability of performing neighborhood search based on vegetative reproduction, and a simulated annealing acceptance probability is introduced to escape local optima.

6. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in claim 5, characterized in that: The replacement using the best record in the historical iteration includes, when the diffusion search strategy is improved, recording the position of the population individual in each iteration by improving the occultation mechanism; if the fitness of the population individual does not improve after a preset number of consecutive iterations, waking up the historical global best population individual to replace the current population individual, and resetting the occultation record counter.

7. The optimized scheduling method for wind-solar-storage-diesel microgrids based on the improved MGO algorithm as described in any one of claims 1, 2, and 6, characterized in that: The optimal solution for each individual in the population is output by: calculating the fitness of each individual in the population based on the updated position of each individual in the population; determining the position and fitness of the individual with the lowest fitness; updating the number of iterations; and when the number of iterations reaches the maximum number of iterations, outputting the position of the individual with the lowest fitness in the population during the historical iterations.

8. A wind-solar-storage-diesel microgrid optimization scheduling model based on an improved MGO algorithm, employing the wind-solar-storage-diesel microgrid optimization scheduling method based on an improved MGO algorithm as described in any one of claims 1 to 7, characterized in that: This includes establishing day-ahead optimization models based on various distributed energy sources in the microgrid, solving the day-ahead optimization models using an improved MGO algorithm, and obtaining day-ahead optimization scheduling strategies.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the wind-solar-storage-diesel microgrid optimization scheduling method based on the improved MGO algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind-solar-storage-diesel microgrid optimization scheduling method based on the improved MGO algorithm as described in any one of claims 1 to 7.

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