CN116131321A

CN116131321A - Micro-grid capacity optimization configuration optimization method and device

Info

Publication number: CN116131321A
Application number: CN202211606877.6A
Authority: CN
Inventors: 杜晓东; 曾四鸣; 赵建利; 罗蓬; 刘成龙; 张文静; 陈泽; 王庚森
Original assignee: Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd; State Grid Hebei Electric Power Co Ltd
Current assignee: Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd; State Grid Hebei Electric Power Co Ltd
Priority date: 2022-12-14
Filing date: 2022-12-14
Publication date: 2023-05-16

Abstract

The embodiment of the disclosure provides a micro-grid capacity optimization configuration optimization method and device, wherein the method comprises the following steps: establishing a micro-grid system model, multiple objective functions and constraint conditions of a micro-grid; constructing a multi-objective optimization configuration model according to the micro-grid system model, the multi-objective function and constraint conditions of the micro-grid; the method can effectively solve the problems of intermittence and randomness of wind energy and solar energy renewable energy sources, improves the performance and the economy of a micro-grid system, and is beneficial to long-term safe, stable and efficient operation of the micro-grid.

Description

Micro-grid capacity optimization configuration optimization method and device

Technical Field

The disclosure relates to the field of micro-grids, in particular to the technical field of micro-grid capacity optimization configuration optimization, and particularly relates to a micro-grid capacity optimization configuration optimization method and device.

Background

Wind power generation and photovoltaic power generation rapidly develop, but randomness and fluctuation bring great difficulty to the planning operation of a micro-grid, the micro-grid is limited by capacity and scale and is influenced by internal distributed power supply and load fluctuation, and the situation is particularly obvious when the micro-grid independently operates, and the micro-grid has the problems of large fluctuation of output power and difficult control.

One of the conventional methods for solving the problem of wind power fluctuation is to configure an energy storage battery with a certain capacity, but the capacity of the energy storage battery cannot be sufficiently large at the present stage, and the environmental pollution and the safety problem caused by the capacity cannot be solved, the energy storage is taken as an important component of a micro-grid, the stable control of the micro-grid is realized, the intermittent and fluctuating power supply output functions are balanced, the energy storage needs to meet the requirements of the micro-grid in terms of energy and power, the energy storage needs to have enough power to meet the short-time overlarge power, and the long-time energy is supported in a time scale, so that the electric energy quality improvement and the stable control function of the micro-grid are difficult to realize by a single energy storage as a whole.

In the prior art, capacity configuration optimization of hybrid energy storage is rarely considered, and for a micro-grid, the probability of system frequency and voltage stability problems caused by single disturbance is increased due to small system scale and poor disturbance resistance, so that capacity optimization configuration of the micro-grid is a multi-objective nonlinear complex problem.

In the capacity optimization configuration optimization of the micro-grid, a linear weighted summation method is adopted to convert a multi-objective function into a single-objective function, however, the selection of the weighting coefficients has human factors, and the problem of unreasonable selection of the weighting coefficients exists.

Disclosure of Invention

The disclosure provides a micro-grid capacity optimization configuration optimization method and device.

According to a first aspect of the present disclosure, there is provided a micro-grid capacity optimization configuration optimization method, the method including:

establishing a micro-grid system model, multiple objective functions and constraint conditions of a micro-grid;

constructing a multi-objective optimization configuration model according to the micro-grid system model, the multi-objective function and constraint conditions of the micro-grid;

and solving the multi-objective optimal configuration model by adopting a mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an objective function of the multi-objective optimal configuration model.

Aspects and any one of the possible implementations as described above, further provide an implementation, the micro grid system model, including: wind power generator model, photovoltaic power generation model, diesel generator model and energy storage system model, wherein, energy storage system model includes: battery model and supercapacitor model.

Aspects and any one of the possible implementations as described above, further provide an implementation, the multi-objective function including: an economic objective function and a reliability objective function;

wherein the economic objective function C _A ＝C _F +C _OM +C _R +C _EC -S；

In the formula, CA is an economic objective function, CF is the initial investment cost of each unit of the micro-grid, COM is the running maintenance cost of the micro-grid system, CR is the replacement cost of the micro-grid system, CEC is the environmental pollution cost of the micro-grid system and S is the residual value of the micro-grid system;

the reliability objective function λ= (1-T _LPSP )·(1-η _LPSP )

Where λ is a reliability objective function, T _LPSP Is the annual power loss time probability sum eta _LPSP Is the annual load shedding probability.

Aspects and any one of the possible implementations as described above, further provide an implementation, the constraint includes:

the micro-grid unit capacity size constraint, the energy storage system charge-discharge constraint, the operation balance constraint and the power supply reliability constraint, wherein the operation balance constraint comprises: electric power balance constraint and energy storage charge-discharge balance constraint.

The above aspect and any possible implementation manner further provide an implementation manner, where the solving the constructed multi-objective optimization configuration model by using the mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an objective function of the multi-objective optimization configuration model includes:

step 1: respectively initializing a firefly algorithm FA and a particle swarm optimization algorithm PSO;

step 2: searching for respective optimal values using the FA and the PSO, respectively;

step 3: setting the iteration interval of sharing as mg, if the sharing time is reached, entering step 4, otherwise jumping to step 5;

step 4: replacing each worst m individuals of the FA and the PSO with the best m individuals of the other;

step 5: after the first generation evolution is finished, storing the better optimal values in the FA and the PSO;

step 6: judging whether an ending condition is met, if so, terminating iteration and outputting an optimal solution; if not, the process jumps to step 2.

solving the constructed multi-objective optimization configuration model by adopting a mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an optimization variable in an objective function of the multi-objective optimization configuration model;

the optimization variables include: the number of wind driven generators, the number of photovoltaic cells, the number of diesel generators, the number of storage batteries and the number of super capacitors.

According to a second aspect of the present disclosure, there is provided a micro grid capacity optimization configuration optimizing apparatus, the apparatus comprising:

the building module is used for building a micro-grid system model, multiple objective functions and constraint conditions of the micro-grid;

the construction module is used for constructing a multi-objective optimization configuration model according to the micro-grid system model, the multi-objective function and constraint conditions of the micro-grid;

the acquisition module is used for solving the multi-objective optimization configuration model by adopting a mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an objective function of the multi-objective optimization configuration model.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first and/or second aspects of the present disclosure.

It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:

FIG. 1 illustrates a flow chart of a microgrid capacity optimization configuration optimization method according to an embodiment of the present disclosure;

FIG. 2 illustrates a block diagram of a microgrid capacity optimization configuration optimization device, according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of a hybrid firefly-particle swarm optimization algorithm, according to an embodiment of the present disclosure;

fig. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.

In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

In the disclosure, an intelligent power consumption peak period judging and distributing method is provided, and accurate data is provided for the field of intelligent power distribution.

Fig. 1 shows a flowchart of a microgrid capacity optimization configuration optimization method according to an embodiment of the present disclosure.

As shown in fig. 1, the method 100 for optimizing the capacity optimization configuration of the micro-grid includes:

s101, establishing a micro-grid system model, multiple objective functions and constraint conditions of a micro-grid;

s102, constructing a multi-objective optimization configuration model according to the micro-grid system model, the multi-objective function and constraint conditions of the micro-grid;

and S103, solving the multi-objective optimal configuration model by adopting a mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an objective function of the multi-objective optimal configuration model.

In some embodiments, the microgrid system model comprises: wind power generator model, photovoltaic power generation model, diesel generator model and energy storage system model, wherein, energy storage system model includes: battery model and supercapacitor model.

In some embodiments, the real-time output power P of the wind generator _wt Comprising:

wherein P is _N Is the rated output power of the wind driven generator, v is the wind speed, v _in To cut in wind speed v _out To cut out wind speed and v _N Is the rated wind speed;

the power output of the wind driven generator is obtained according to the wind speed, the curve simulating the wind speed distribution comprises a lognormal distribution, a Reyleigh distribution and a Weibull distribution, wherein the probability density function of the wind speed v comprises the following steps:

wherein k is a shape parameter and c is a scale parameter;

shape parameters

In the formula, v _max For maximum wind speed observed during time T, v _a The average wind speed observed in the time T and T are time;

dimensional parameters

Wherein Γ is Gamma function, k is shape parameter, v _a Is the average wind speed observed during time T;

the wind speed is a function of the height h, the statistical value v of the wind speed data ₀ Is at a reference height H ₀ The actual wind speed v (t) at the hub of the wind driven generator is measured, and comprises:

v(t)＝v ₀ (t)(H/H ₀ ) ^a

wherein H is the height of the tower, H ₀ In order to refer to the height a as the description factor of the surface roughness, the value range of a is 1/7-1/4.

In some embodiments, the output power of the photovoltaic power generation system is determined by local illumination data, photovoltaic system parameters, and environmental temperature factors;

simulating the output power characteristic of a photovoltaic power generation system, firstly, simulating illumination intensity by considering randomness and time variability, then obtaining output power according to the output characteristic of the photovoltaic power generation system, wherein the long-term illumination intensity is load Beta distribution, and the shape parameter Beta of probability density Beta distribution comprises the following components:

wherein G is the actual illumination intensity in the current period, gmax is the maximum illumination intensity in the current period, Γ is Gamma function, alpha and Beta are the shape parameters of Beta distribution, and the shape parameters alpha of Beta distribution comprise:

the shape parameter β of the Beta distribution, comprising:

wherein mu is the average value of illumination intensity in local statistical time and sigma is the variance of illumination intensity in local statistical time;

the generating capacity of the photovoltaic cell panel is in a decreasing trend along with the increase of the working temperature, and the generating capacity is reduced by 0.35-0.5% when the temperature is increased by 1 ℃ based on 25 ℃, and the output power of the photovoltaic module comprises the following components:

P _pv ＝P _STC F _pv G _AC [1+k(T _w -T _r )]/G _STC

wherein P is _STC Maximum test power under standard test conditions, including: the incident intensity of solar energy is 1kW/m ² And an ambient temperature of 25 ℃, F _pv Derating factor for photovoltaic module work, used for representing dust, rain and snow covering on photovoltaic cell panel surface and loss caused by photovoltaic cell panel aging, F _pv The value range is 0.9-0.95, G _AC G is the illumination intensity _STC Is the illumination intensity under standard test conditions, and has the value of 1kW/m ² K is the power temperature coefficient, and the value is-0.5% to-0.35%/K, T _w For the working temperature of the battery plate, T _r The reference temperature was 25 ℃.

In some embodiments, the process of generating electric energy by the diesel generator is to convert chemical energy of fuel into mechanical energy of the internal combustion engine, and then convert the mechanical energy into electric energy by the internal combustion engine, so that the diesel generator has small volume compared with other power generation equipment, saves space, is quick to start, is flexible to control, is convenient to carry, takes the diesel engine as an emergency or standby power supply in the micro-grid, and simultaneously provides operation voltage and frequency reference for the system when the micro-grid independently operates;

the output power of the quasi-static model of the diesel generator is continuously adjustable within the range from 0 to rated power, and the fuel consumption V of the diesel generator during operation _dis (t) comprising:

V _dis (t)＝cP _G (t)+dP _G0

wherein V is _dis (t) is the fuel consumption of the diesel generator during operation, and c and d are fuel curve coefficients respectively;

the actual output power of the diesel generator is adjusted according to the load demand, but when the diesel generator runs below 30% of rated power, the diesel fuel is insufficiently combusted, the fuel economy is reduced, carbon deposition of the diesel generator is caused, the equipment failure rate is increased, the running life is reduced, the diesel generator is required to run above 30% of rated power, the diesel generator is frequently started and stopped to cause the faults of a spark plug and a cylinder, and the minimum running time constraint needs to be met once the diesel generator is started.

In some embodiments, the total amount of electrical energy E stored by the battery pack _bat Comprising:

E _bat ＝0.001N _bat C _bat U _bat

wherein C is _bat For monomer capacity, U _bat Is rated voltage and N _bat Is the cascade number;

assuming that the depth of discharge of the storage battery is lambda, lambda is greater than 0 and lambda is less than 1, the storage battery can discharge electric energy E in each cycle of charge and discharge _{bat_f} Comprising:

E _{bat_f} ＝0.001N _bat C _bat U _bat λ

wherein N is _bat C is the actual installed number of storage battery power generation systems in the micro-grid _bat For monomer capacity, U _bat Rated voltage and lambda is the depth of discharge of the battery;

electric energy E required by the storage battery during each cycle of charging _{bat_ch} Comprising:

E _{bat_ch} ＝0.001N _bat C _bat U _bat λ/η

wherein N is _bat C is the actual installed number of storage battery power generation systems in the micro-grid _bat For monomer capacity, U _bat As rated voltage, lambda is the depth of discharge of the battery and eta is the charging efficiency of the battery;

considering the storage battery as constant voltage operation, the working current is controlled to be 0.1 unit capacity, and the power output capacity of the storage battery pack comprises:

P _bat ＝0.0001N _bat C _bat U _bat

wherein N is _bat C is the actual installed number of storage battery power generation systems in the micro-grid _bat For monomer capacity and U _bat Is rated voltage.

In some embodiments, the super capacitor adopts a first-order linear RC model, the power P and the current I are negative in charging and positive in discharging, and the voltage U at the end of the super capacitor ₀ Comprising:

U ₀ ＝U-IR _es

wherein R is _es The capacitor is equivalent to series resistance, U is capacitor voltage and I is charge and discharge current;

super capacitor charge-discharge power P includes:

P＝(U-U ₀ )I

in U ₀ The voltage is super capacitor terminal voltage, U is capacitor voltage and I is charge-discharge current;

super capacitor energy output, including:

wherein C is the capacitance of the super capacitor, U _max Is the highest voltage and U of the super capacitor in the charging and discharging process _min Is the lowest voltage of the super capacitor in the charge and discharge process.

In some embodiments, the multi-objective function comprises: an economic objective function and a reliability objective function;

initial investment costs for each unit of the micro-grid, including:

C _F ＝C _WT +C _PV +C _G +C _bat +C _cap

wherein C is _WT C is initial investment cost of wind power generation system _PV C is the initial investment cost of the photovoltaic power generation system _G C is initial investment cost of diesel engine power generation system _bat For initial investment cost and C of battery system _cap Initial investment cost for the supercapacitor system;

system operation maintenance costs, including:

in the method, in the process of the invention,

for the operating maintenance costs of the wind power generator system per unit time, < >>

Cost of operation and maintenance of photovoltaic power generation system per unit time, < >>

Cost of operation and maintenance of diesel power generation system per unit time, < >>

For the operating maintenance costs of the battery system per unit time, < >>

Is the operation and maintenance cost of the super capacitor system in unit time, t _WT For the running time, t, of the wind power generator system _PV For the running time, t, of a photovoltaic power generation system _G For the running time, t, of a diesel engine power generation system _bat For the operating time and t of the battery system _cap The operation time of the supercapacitor system;

fuel costs for a diesel generator, comprising:

C _dis ＝p _dis ×∫V _dis (t)d(t)

wherein V is _dis Fuel consumption and p for diesel generator _dis The price of the diesel oil is the price;

the replacement cost of the system only occurs when the life of the units within the system is less than the engineering age of the system, including:

in the method, in the process of the invention,

for replacement costs of wind power systems, +.>

Replacement cost for photovoltaic power generation system, +.>

Replacement cost for diesel power generation system, +.>

Replacement costs and +.>

Replacement cost for the supercapacitor system;

the environmental pollution cost comes from CO generated by the fuel consumption of the diesel generator ₂ 、SO ₂ And NO ₂ In the micro-grid planning design, the pollution control cost generated by a diesel generator is incorporated into the system cost, and the penalty cost c for consuming fuel oil per liter is converted according to the penalty cost of each pollution gas _ec Cost of environmental pollution C of the system _EC Comprising:

C _EC ＝c _ec ×∫V _dis (t)d(t)

wherein, c _ec Penalty cost and V for consuming fuel per liter _dis The fuel consumption of the diesel generator;

the system residual value S is the recovery value of the system equipment, is related to the system scale, the composition proportion of each unit and the recovery difficulty factor, and can slightly reduce the total cost of the system, but does not influence the optimization process with the minimum total cost of the system;

the economic objective function C _A ＝C _F +C _OM +C _R +C _EC -S；

Wherein C is _A As an economic objective function, C _F For initial investment cost of each unit of micro-grid, C _OM C, running and maintaining cost for micro-grid system _R C is the replacement cost of the micro-grid system _EC The environmental pollution cost for the micro-grid system and S are the residual values of the micro-grid system;

optimizing the capacity of the micro-grid system, namely under the condition that all constraint conditions are met, the actual installed number N of the wind power generation systems _WT Actual installed number N of photovoltaic power generation systems _PV Actual installed number N of diesel engine power generation systems _G Actual installed number N of storage battery power generation systems _bat And the actual installed number N of the super capacitor system _cap In the arrangement and combination of the components, C is preferably selected from _A A minimal combination;

in some embodiments, the annual power loss time probability includes:

wherein S is _loss (t) is a system power failure time identifier, when the value is 1, the system part accords with the power interruption, and when the value is 0, the system part accords with the power consumption requirement and is satisfied;

probability of annual loss of load eta _LPSP Comprising:

wherein E is _loss Is the total annual loss of load and E _tot Is a total annual load demand;

the reliability objective function λ= (1-T _LPSP )·(1-η _LPSP )

Where λ is a reliability objective function, T _LPSP Is the annual power loss time probability sum eta _LPSP Is the annual loss of load probability;

in some embodiments, the constraint includes:

In some embodiments, the microgrid element capacity size constraint comprises:

N _WTmin ≤N _WT ≤N _WTmax

N _PVmin ≤N _PV ≤N _PVmax

N _Gmin ≤N _G ≤N _Gmax

N _batmin ≤N _bat ≤N _batmax

N _capmin ≤N _cap ≤N _capmax

wherein N is _WTmin Minimum allowable number of wind power generation system in micro-gridNumber, N _PVmin Is the minimum allowable number of photovoltaic power generation systems in the micro-grid and N _Gmin Is the minimum allowable number of diesel engine power generation systems in the micro-grid and N _batmin For minimum allowable number of storage battery power generation systems in micro-grid and N _capmin The minimum allowable number of the super capacitor systems in the micro-grid is set; n (N) _WT For the actual installed number N of wind power generation systems in a micro-grid _PV Is the actual installed number N of the photovoltaic power generation systems in the micro-grid _G For the actual installed number N of diesel engine power generation systems in the micro-grid _bat For the actual installed number N of storage battery power generation systems in the micro-grid _cap The actual installed number of the super capacitor systems in the micro-grid; n (N) _WTmax Is the maximum allowable number N of wind power generation systems in the micro-grid _PVmax Is the maximum allowable number N of photovoltaic power generation systems in the micro-grid _Gmax Is the maximum allowable number of diesel engine power generation systems in the micro-grid and N _batmax For the maximum allowable number N of storage battery power generation systems in the micro-grid _capmax The maximum allowable number of the super capacitor systems in the micro-grid is set;

the energy storage system charge-discharge constraint comprises:

S _ocmin ≤S _oc ≤S _ocmax

0≤I _ch ≤I _chmax

0≤I _dch ≤I _dchmax

0≤P _ch ≤P _chmax

0≤P _dch ≤P _dchmax

wherein S is _ocmin Is the upper limit and S of the state of charge of an energy storage system in a micro-grid _ocmax Is the lower limit of the state of charge of an energy storage system in a micro-grid, I _chmax Charging maximum current sum I for energy storage systems in micro-grid _dchmax Discharging maximum current, P, for energy storage system in micro-grid _chmax Charging maximum power and P for energy storage systems in micro-grid _dchmax Discharging maximum power for an energy storage system in the micro-grid;

the electric power balancing constraint comprises:

P _Load (t)＝P _WT (t)+P _PV (t)+P _G (t)+P _bat (t)+P _cap (t)

wherein P is _WT (t) is the power of the wind power generation system in the micro-grid, P _PV (t) is the power of the photovoltaic power generation system in the micro-grid, P _G (t) is the power of the diesel power generation system in the micro-grid, P _bat (t) is the power of a storage battery power generation system in the micro-grid, P _cap (t) is the power of the supercapacitor system in the microgrid;

the energy storage charge-discharge balance constraint comprises:

E _ch ＝E _dch

wherein E is _ch Charging energy sum E for energy storage systems in micro-grids _dch Discharging energy of an energy storage system in the micro-grid;

the power supply reliability constraint includes:

f _LPSP ≤f _LPSPmax

wherein P is _WT (t) is the power of the wind power generation system in the micro-grid, P _PV (t) is the power of the photovoltaic power generation system in the micro-grid, P _G (t) is the power of the diesel power generation system in the micro-grid, P _bat (t) is the power of a storage battery power generation system in the micro-grid, P _cap (t) is the power of the supercapacitor system in the microgrid, P _load (T) is the power sum T of the loads in the microgrid as a period;

in some embodiments, the method for solving the constructed multi-objective optimization configuration model by adopting the mixed firefly-particle swarm optimization algorithm to obtain an optimal solution of an objective function of the multi-objective optimization configuration model includes:

step 3: if the sharing time is reached, entering a step 4, otherwise jumping to a step 5;

According to the embodiment of the disclosure, the following technical effects are achieved:

the energy storage system is formed by mutually coupling and interconnecting the energy storage battery of the energy type energy storage device and the super capacitor of the power type energy storage device, meanwhile, the distributed power capacity and the energy storage system capacity in the micro-grid planning are jointly solved, the multi-objective optimization configuration is carried out, the capacity of the micro-grid is expanded, the effects of peak clipping and valley filling and stabilizing power fluctuation are achieved by expanding the micro-grid, the problems of intermittence and randomness of renewable energy sources of wind energy and solar energy can be effectively solved, the system performance and economy are improved, and long-term safe, stable and efficient operation of the independent micro-grid is facilitated.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.

The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.

Fig. 2 shows a block diagram of a microgrid capacity optimization configuration optimization device 200, according to an embodiment of the present disclosure.

As shown in fig. 2, the apparatus 200 includes:

a building module 201, configured to build a micro-grid system model, multiple objective functions, and constraint conditions of a micro-grid;

a construction module 202, configured to construct a multi-objective optimization configuration model according to the micro-grid system model, the multi-objective function, and constraint conditions of the micro-grid;

and the obtaining module 203 is configured to solve the multi-objective optimization configuration model by adopting a mixed firefly-particle swarm optimization algorithm, so as to obtain an optimal solution of an objective function of the multi-objective optimization configuration model.

FIG. 3 illustrates a flow chart of a hybrid firefly-particle swarm optimization algorithm 300, according to an embodiment of the present disclosure;

as shown in fig. 3, the hybrid firefly-particle swarm optimization algorithm 300 comprises:

s301: for a firefly algorithm FA and a particle swarm optimization algorithm PSO, the population numbers and the individual dimensions of the FA and the PSO are the same, and the objective functions of the FA and the PSO are the same;

s302: searching respective optimal values by using the FA and the PSO, wherein the FA and the PSO are independently evolved, and the FA and the PSO respectively store optimal individuals of own groups;

s303: if the sharing time is reached, the step S304 is entered, otherwise, the step S305 is skipped;

s304: bringing the FA and theThe m individuals with the worst PSO are replaced by the m individuals with the best counterpart, the value of m is required to be less than half of the number of individuals of the family, after the mixed firefly-particle swarm optimization algorithm evolves for w generations, the diversity of the family is improved, the value of m is gradually increased, and the formula is adopted

Wherein N is the number of groups, t is the current evolution algebra, t _max R is the maximum evolution algebra ₀ For proportional control constant, sign

Representing a downward integer;

when m individuals with worst FA and PSO are replaced by m individuals with best counterpart, in the FA, the position and objective function value need to be updated, in the PSO, speed information needs to be additionally stored, the position and objective function value of the PSO individual are directly replaced by the individual position and objective function value of the FA, the required speed information is created in the FA, and in the mixed firefly-particle swarm optimization algorithm, the speed information is obtained by using the formula y _i (t)＝μy _i (t-1)(1-y _i (t-1)) to construct the required velocity sequence:

wherein y is _i (t) is [0,1]Wherein t is evolution algebra and mu is control parameter;

s305: after the first generation evolution is finished, storing the better optimal values in the FA and the PSO;

s306: judging whether an ending condition is met, if so, stopping iteration and outputting an optimal solution, wherein the optimal solution is a better optimal value in both FA and PSO; if not, the process proceeds to step S302.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

The electronic device 400 comprises a computing unit 401 that may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In RAM403, various programs and data required for the operation of device 400 may also be stored. The computing unit 401, ROM 402, and RAM403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

Various components in electronic device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM 402 and/or the communication unit 409. One or more of the steps of method 400 described above may be performed when a computer program is loaded into RAM403 and executed by computing unit 401. Alternatively, in other embodiments, computing unit 401 may be configured to perform method 400 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A micro-grid capacity optimization configuration optimization method comprises the following steps:

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the micro-grid system model comprises: wind power generator model, photovoltaic power generation model, diesel generator model and energy storage system model, wherein, energy storage system model includes: battery model and supercapacitor model.

3. The method of claim 1, wherein the multi-objective function comprises: an economic objective function and a reliability objective function;

wherein the economic objective function C _A ＝C _F +C _OM +C _R +C _EC -S；

the reliability objective function λ= (1-T _LPSP )·(1-η _LPSP )；

4. The method of claim 1, wherein the constraint comprises:

5. The method of claim 1, wherein the solving the constructed multi-objective optimization configuration model using the hybrid firefly-particle swarm optimization algorithm to obtain an optimal solution for the objective function of the multi-objective optimization configuration model comprises:

6. The method of claim 1, wherein the solving the constructed multi-objective optimization configuration model using the hybrid firefly-particle swarm optimization algorithm to obtain an optimal solution for the objective function of the multi-objective optimization configuration model comprises:

7. A microgrid capacity optimization configuration optimization device, comprising:

8. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.