CN113285449A - Intelligent economic dispatching method considering multi-microgrid joint dispatching optimization - Google Patents

Intelligent economic dispatching method considering multi-microgrid joint dispatching optimization Download PDF

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CN113285449A
CN113285449A CN202110580379.8A CN202110580379A CN113285449A CN 113285449 A CN113285449 A CN 113285449A CN 202110580379 A CN202110580379 A CN 202110580379A CN 113285449 A CN113285449 A CN 113285449A
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CN113285449B (en
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鄢仁武
刘蕴樟
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses an intelligent economic dispatching method considering multi-microgrid joint dispatching optimization, which comprises the following steps of: the intelligent economic dispatching algorithm converts the output power of the controllable distributed power supplies of the micro-grids into an unconstrained cost objective function, and can quickly calculate the dispatching instruction of the controllable distributed power supply with the optimal objective function. The method and the device are easy to realize, provide the optimal control scheduling instruction considering the multiple micro-grids on the premise of meeting the load power consumption quality, and realize that the coordinated scheduling between the multiple micro-grids and the power grid has higher economical efficiency and benefit. The invention has the following advantages: the algorithm has the advantages of simple structure, easy understanding, high convergence rate and high solving precision, and effectively reduces the comprehensive operation cost. The invention has a common optimization algorithm: the basic particle swarm algorithm and the improved particle swarm algorithm have more advantages.

Description

Intelligent economic dispatching method considering multi-microgrid joint dispatching optimization
Technical Field
The invention relates to the technical field of microgrid power supply scheduling, in particular to an intelligent economic scheduling method considering multi-microgrid joint scheduling optimization.
Background
With the rapid development of global economy, the demand of energy sources of all countries is increasing day by day, however, petroleum, coal and natural gas are gradually exhausted, and an unprecedented energy crisis is led globally. Energy, environment and development become problems to be solved urgently in the international society, China makes a promise to the world: efforts were strived to achieve the goal of "carbon neutralization" before 2060. "carbon neutralization" shall be a means of promoting technological, economic development, and shall not be a cumbrous of inhibiting development. The development of power systems is being examined simultaneously by the increasingly severe energy crisis and environmental problems, and thus renewable energy is being actively developed and utilized in all countries of the world. The micro-grid with the advantages of safety, reliability, energy conservation, environmental protection, good economic benefit and the like becomes an important component for promoting the development of electric power. The micro-grid is a small-sized grid system with self-control capability and composed of a plurality of distributed power sources and energy storage units, meets the requirements of pursuing sustainable energy and low-carbon development in the world at present, and becomes a research hotspot of experts and scholars in the power industry and the energy industry.
A typical microgrid is mainly composed of: the system comprises distributed power supplies such as wind power, photovoltaics, diesel generators, fuel cells, micro gas engines and the like and an energy storage system, and is shown in figure 1. The optimization of the micro-grid scheduling is the basis for ensuring the micro-grid operation economy and environmental friendliness. At present, the intelligent economic dispatching algorithm aiming at the micro-grid focuses more on discussion and research on a single micro-grid, and the intelligent economic dispatching algorithm considering the multi-micro-grid joint dispatching optimization is less.
Disclosure of Invention
The invention aims to provide an intelligent economic dispatching method considering multi-microgrid joint dispatching optimization.
The technical scheme adopted by the invention is as follows:
an intelligent economic dispatching method considering multi-microgrid joint dispatching optimization comprises the following steps:
(1) the intelligent economic dispatching algorithm converts the output power of the controllable distributed power supplies of the micro-grids into an unconstrained cost objective function, and can quickly calculate the dispatching instruction of the controllable distributed power supply with the optimal objective function. The optimal objective function means that the comprehensive operation cost is lowest:
Figure RE-GDA0003120068900000011
in the formula: t represents the total optimization duration of the model; n represents the number of the model micro-grids;
Figure RE-GDA0003120068900000012
for the cost of electricity purchase;
Figure RE-GDA0003120068900000013
cost for distributed power supply operation;
Figure RE-GDA0003120068900000014
cost of maintenance for distributed power supplies;
Figure RE-GDA0003120068900000015
cost for pollution control;
Figure RE-GDA0003120068900000016
for the benefit of selling electricity.
(2) The intelligent economic dispatching method flow chart is shown in fig. 2, and comprises the following steps:
step 1: setting algorithm parameters and initializing species initial generation population;
step 2: calculating the population fitness of each species, recording the current population position and speed, marking the optimal fitness population of each species and the global optimal fitness population, and setting the iteration time t as 1;
and step 3: judging whether the iteration times T meet a preset iteration threshold value T or not; if yes, entering step 10; if not, entering the step 4;
and 4, step 4: judging whether the reproduction times G meet a preset reproduction threshold value G or not; if yes, entering step 9; if not, entering the step 5;
and 5: judging whether the current species belongs to a global optimal species;
if yes, updating the moving speed and the position parameters of the species according to a position coordinate updating formula (5-17) and a speed updating formula (5-18) of the global optimal fitness population, and executing the step 7;
if not, entering the step 6;
step 6: judging whether the current species belongs to the optimal fitness population of the corresponding species;
if yes, updating the moving speed and the position parameters of the species according to a position coordinate updating formula (5-17) and a speed updating formula (5-19) of the optimal fitness population of the affiliated species, and executing the step 7;
if not, updating the species moving speed and the location parameters according to a location coordinate updating formula (5-17) and a speed updating formula (5-20) of the common population and executing the step 7;
and 7: and calculating the fitness of all the populations of each species, and recording the current population position and speed. Marking the population with the optimal fitness and the population with the maximum fitness change rate;
and 8: calculating the fitness of the overall fitness optimal population and the fitness change rate maximum population to reproduce a new offspring population according to the fitness rise rate maximum population reproduction formula, the fitness decline rate maximum population reproduction formula and the overall fitness optimal population reproduction formula; meanwhile, updating the time t to t +1, and the number of times of reproduction g to g + 1;
and step 9: setting the number g of reproduction times to zero, and taking the optimal fitness population of each species as the initial generation population of the next iteration;
step 10: and outputting the position parameters and the fitness values of the globally optimal individuals.
By adopting the technical scheme, the invention has the following advantages: the algorithm is simple in structure, easy to understand, high in convergence speed and high in solving precision, controllable distributed power supply output power in each microgrid can be reasonably planned, and the comprehensive operation cost of the microgrid system is effectively reduced. The rationality of the intelligent economic dispatching algorithm is proved by establishing and simulating a power model considering multiple micro-grids, and simultaneously the algorithm is similar to a common optimization algorithm: the basic particle swarm algorithm and the improved particle swarm algorithm are compared to prove that the method has more superiority and high efficiency.
Drawings
The invention is described in further detail below with reference to the accompanying drawings and the detailed description;
FIG. 1 is a schematic diagram of a typical grid-connected microgrid model of the prior art;
FIG. 2 is a schematic flow chart diagram illustrating an intelligent economic dispatch method for optimizing multi-microgrid joint dispatch in accordance with the present invention;
fig. 3 is a schematic diagram of a scheduling strategy of a multi-microgrid system model;
fig. 4 is a schematic diagram of a micro-grid-one micro-source output optimization result in an optimization example of the present invention;
fig. 5 is a schematic diagram of a micro-source output optimization result of a second microgrid in an optimization example of the present invention;
fig. 6 is a schematic diagram of a micro-source output optimization result of a micro-grid three in the optimization example of the present invention;
FIG. 7 is a comparison of the cost of operation of the three algorithms.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
As shown in fig. 1, the invention discloses an intelligent economic dispatching method for multi-microgrid joint dispatching optimization, which converts the output power of controllable distributed power supplies of a plurality of microgrids into an unconstrained cost objective function, and rapidly calculates to obtain a dispatching instruction of the controllable distributed power supply with the optimal objective function, wherein the optimal objective function means the lowest comprehensive operation cost,
the concrete formula with the lowest comprehensive operation cost is
Figure RE-GDA0003120068900000031
In the formula: t represents the total optimization duration of the model; n represents the number of the model micro-grids;
Figure RE-GDA0003120068900000032
for the cost of electricity purchase;
Figure RE-GDA0003120068900000033
cost for distributed power supply operation;
Figure RE-GDA0003120068900000034
cost of maintenance for distributed power supplies;
Figure RE-GDA0003120068900000035
cost for pollution control;
Figure RE-GDA0003120068900000036
for the benefit of selling electricity.
The multi-microgrid system operation model comprises: the model consists of three small-sized micro-grids, each micro-grid comprises a photovoltaic generator, a wind driven generator, a storage battery energy storage system and controllable micro-sources in different forms, and the model is shown in a table 5-1.
Because the photovoltaic power generation and the wind power generation do not consume fuel and produce pollution, the photovoltaic power generation and the wind power generation are preferentially considered when each micro-grid system operates, and the wind power generation and the photovoltaic power generation work in the maximum power generation state. Because photovoltaic and wind power generation are limited by local natural resources, in order to improve the stability and flexibility of system output power and improve the load power consumption quality, each microgrid is provided with a storage battery energy storage system and controllable micro sources in different forms. The storage battery energy storage system can play a role in power complementation and load balancing, and the principle is to convert chemical energy into electric energy; the controllable micro sources in different forms are mainly used as backup power generation equipment to provide energy guarantee in time.
Micro-grid 1 Microgrid II Microgrid III
Photovoltaic power generation Photovoltaic power generation Photovoltaic power generation
Wind power generation Wind power generation Wind power generation
Storage battery energy storage system Storage battery energy storage system Storage battery energy storage system
Fuel cell Micro gas turbine Fuel cell
TABLE 5-1 Multi-microgrid model Structure composition
The micro-grid stable operation constraint condition is as follows:
(1) and (3) a controllable micro-source output power constraint condition:
Figure RE-GDA0003120068900000041
Figure RE-GDA0003120068900000042
Figure RE-GDA0003120068900000043
Figure RE-GDA0003120068900000044
in the formula:
Figure RE-GDA0003120068900000045
respectively representing the actual output power of the fuel cell and the micro gas turbine at the moment t of the ith microgrid;
Figure RE-GDA0003120068900000046
Figure RE-GDA0003120068900000047
representing the upper and lower output limits of the fuel cell stack;
Figure RE-GDA0003120068900000048
representing the upper and lower output limits of the micro gas turbine;
Figure RE-GDA0003120068900000049
Figure RE-GDA00031200689000000410
respectively representing the upper limit and the lower limit of the output power climbing of the fuel cell and the micro gas turbine.
(2) And (3) charge and discharge constraint conditions of the storage battery energy storage system:
Figure RE-GDA00031200689000000411
SOCmin≤SOCi,t≤SOCmax (5-6)
in the formula:
Figure RE-GDA00031200689000000412
the actual charging and discharging power of the storage battery energy storage system at the ith microgrid t moment is represented; SOCi,tThe actual charge quantity of the storage battery energy storage system at the moment t of the ith microgrid is represented;
Figure RE-GDA00031200689000000413
representing the upper and lower limits of the charging and discharging power of the energy storage battery; SOCmaxAre respectively provided withRepresents SOCminThe safe charge capacity of the energy storage battery is limited.
(3) When the storage battery energy storage system is charged and discharged, the battery charge at the time t +1 can be expressed as:
Figure RE-GDA00031200689000000414
in the formula:
Figure RE-GDA00031200689000000415
the charging and discharging power of the ith microgrid at the moment t is represented; w represents the total capacity of the battery energy storage system.
(4) The microgrid system power balance formula is as follows:
Figure RE-GDA00031200689000000416
in the formula:
Figure RE-GDA00031200689000000417
the total power purchased from the ith microgrid to the power grid at the moment t is represented;
Figure RE-GDA00031200689000000418
the total power of electricity sold to the power grid by the ith microgrid at the moment t is represented;
Figure RE-GDA00031200689000000419
the total output power of the ith micro gas turbine of the microgrid at the moment t is represented;
Figure RE-GDA00031200689000000420
the total output power of the ith micro-grid fuel cell at the moment t is represented;
Figure RE-GDA0003120068900000051
the total power of the wind power generation of the ith microgrid at the moment t is represented;
Figure RE-GDA0003120068900000052
the total power of the ith microgrid photovoltaic power generation at the time t is represented;
Figure RE-GDA0003120068900000053
and the total charging and discharging power of the ith microgrid energy storage system at the moment t is represented.
The micro-grid optimizes the economic operation target:
the optimization of economic operation aims at reducing comprehensive operation cost, and mainly comprises operation cost, maintenance cost and pollution control cost of each distributed power supply, electricity purchasing cost and electricity selling income of a power grid.
Figure RE-GDA0003120068900000054
(1) Because photovoltaic and wind power generation belong to clean energy and basically have no power generation operation cost and power generation pollution control cost, the operation cost of the micro-grid system mainly comprises controllable distributed power supplies of each micro-grid. The operating costs of the fuel cell and the micro gas turbine are shown by the following formula:
Figure RE-GDA0003120068900000055
Figure RE-GDA0003120068900000056
Figure RE-GDA0003120068900000057
in the formula:
Figure RE-GDA0003120068900000058
representing the power generation operation cost of the ith microgrid at the moment t; cng、ci fuel、ηfc、ηmtRespectively representing fuel cost coefficient of fuel cell, fuel cost coefficient of micro gas turbine, power generation efficiency of fuel cell, and micro gas turbineThe power generation efficiency; l isHVGNThe low heat value of the natural gas is expressed, and the value is generally 9.78 (kW.h)/m3。
(2) The cost of generating electricity and treating pollution means that CO can be discharged in the running process of equipment2、NOxAnd the pollution gases such as CO and the like are in a unified dimension and need to be converted into pollution treatment cost for treatment. Cost of pollution control
Figure RE-GDA0003120068900000059
And the output power of the controllable distributed power supply of each microgrid is related. Because photovoltaic and wind power generation belong to clean energy, pollution control cost is mainly caused by a micro gas turbine and a fuel cell, and the following formula is shown:
Figure RE-GDA00031200689000000510
in the formula: j is the type number of the pollutant, and is respectively CO2、NOx、CO；
Figure RE-GDA00031200689000000511
The pollution control cost of the ith microgrid at the moment t is expressed;
Figure RE-GDA00031200689000000512
respectively representing the discharge coefficients of different pollutants and the pollution control cost coefficients of different pollutants.
(3) The maintenance cost mainly comprises the maintenance cost of the distributed power supply and the breakage cost of the energy storage battery.
Figure RE-GDA0003120068900000061
In the formula:
Figure RE-GDA0003120068900000062
the maintenance cost of the ith microgrid at the moment t is represented; sigmawt、σpv、σfc、σmt、σbtRepresenting wind turbine, photovoltaic array, fuel cell, microMaintenance costs of gas turbines, energy storage batteries;
Figure RE-GDA0003120068900000063
respectively representing wind-solar predicted power and stored energy charge and discharge power.
(4) Each microgrid has energy transfer with a power grid according to a scheduling strategy, so the comprehensive operation cost also comprises electricity purchasing cost and electricity selling income, and the specific formula is as follows:
Figure RE-GDA0003120068900000064
Figure RE-GDA0003120068900000065
in the formula:
Figure RE-GDA0003120068900000066
respectively showing the actual electricity purchasing price and the electricity selling price at the time t;
Figure RE-GDA0003120068900000067
and respectively representing the power purchased and sold from the power grid at the ith microgrid at the moment t.
The intelligent economic dispatching algorithm comprises the following steps: the scheduling problem of a plurality of micro-grids is a complex optimization problem with multiple targets, multiple constraints and strong nonlinearity, the traditional mathematical optimization algorithm is difficult to work, and the intelligent scheduling algorithm provided by the invention can reasonably and efficiently solve the problem.
The intelligent economic dispatch Algorithm is a group intelligent Algorithm which is inspired by the fact that natural organisms find habitats more suitable for living and continuously migrate and multiply, and is provided by simulation and emulation, and is called Migration and multiplication Algorithm (MARA). The MARA algorithm analogizes the solution space of the optimization problem to the natural environment where the species live; the population abstraction is a coordinate point in a solution space and is used for representing a feasible solution of the complex problem; the judgment of the goodness and badness of the feasible solution is analogized to the evaluation of the population to the habitat and is expressed by the fitness. The species mentioned in the present invention refers to a group of the same species, different populations. The MARA algorithm sets a simple migration strategy and a simple propagation rule for each population, so that each population can show the characteristics of migration and propagation. The search of the solution space is realized through the migration behavior of the population, and meanwhile, the diversity of feasible solutions is realized through the propagation behavior of the population.
The MARA algorithm has the characteristics of memory capability and an information sharing mechanism, and can be interpreted as a co-occurrence cooperative behavior. Each species is able to remember historical local optimal habitats for the affiliated species as a direct experience shared within the population of the affiliated species. Meanwhile, local optimal habitats of all species are comprehensively considered, and a global optimal habitat is selected preferentially to be shared as indirect experience for all species. And dynamically adjusting the migration strategy of the population in the natural environment through a sharing mechanism. I.e. the migration behavior of the population is influenced by the direct experience of the species to which it belongs and guided by the indirect experience of nature. Finally, all species can migrate to habitats rich in livable habitats to take root for reproduction, and finally convergence on the global optimal solution is realized.
The MARA algorithm has the characteristics of population multiplication: when the population of each species migrates, the population of some species will selectively reproduce new offspring populations based on the evaluation of the fitness of the new habitat. The population propagation is beneficial to expanding the scale of species, exploring a new habitat environment and increasing the diversity of feasible solutions, thereby avoiding the algorithm from falling into local optimization. The MARA algorithm has a naturally chosen mechanism: natural selection refers to the phenomenon that the suitable people live and the uncomfortable people are eliminated during the living and fighting of organisms. The evaluation of species population to new habitat changes in the process of participating in migration and multiplication, the species scale is continuously enlarged, and finally, part of species population is eliminated by nature due to the fact that the species population is not suitable for the survival conditions of the new habitat. The natural selection mechanism is beneficial to improving the solving efficiency of the algorithm.
As shown in fig. 2, the intelligent economic dispatch method includes the following steps:
step 1: setting algorithm parameters and initializing species initial generation population. The parameters include: spatial dimension D, number of species N; breeding the number G of filial generation populations by the population with the maximum fitness rise rate; breeding the number P of filial generation populations by the population with the maximum fitness decline rate; breeding the number U of offspring population by the global optimal population; the maximum iteration time Tmax; maximum number of propagations Gmax.
Step 2: calculating the population fitness of each species, recording the current population position and speed, marking the optimal fitness population of each species and the global optimal fitness population, and setting the iteration time t as 1;
and step 3: judging whether the iteration times T meet a preset iteration threshold value T or not; if yes, entering step 10; if not, entering the step 4;
and 4, step 4: judging whether the reproduction times G meet a preset reproduction threshold value G or not; if yes, entering step 9; if not, entering the step 5;
and 5: judging whether the current species belongs to a global optimal species;
if yes, updating the species moving speed and the location parameters according to the location coordinate updating formula and the speed updating formula of the global optimal fitness population, and executing the step 7;
if not, entering the step 6;
step 6: judging whether the current species belongs to the optimal fitness population of the corresponding species;
if yes, updating the moving speed and the position parameters of the species according to the position coordinate updating formula and the speed updating formula of the optimal fitness population of the subordinate species, and executing the step 7;
if not, updating the moving speed and the position parameters of the public updated species according to the position coordinate updating formula and the speed of the common population and executing the step 7;
and 7: and calculating the fitness of all the populations of each species, and recording the current population position and speed. Marking the population with the optimal fitness and the population with the maximum fitness change rate;
and 8: calculating the fitness of the overall fitness optimal population and the fitness change rate maximum population to reproduce a new offspring population according to the fitness rise rate maximum population reproduction formula, the fitness decline rate maximum population reproduction formula and the overall fitness optimal population reproduction formula; meanwhile, updating the time t to t +1, and the number of times of reproduction g to g + 1;
and step 9: setting the number g of reproduction times to zero, and taking the optimal fitness population of each species as the initial generation population of the next iteration;
step 10: and outputting the position parameters and the fitness values of the globally optimal individuals.
The algorithm of the invention is divided into: migration, reproduction and natural selection.
1) Migration: each population has two attribute information: position coordinates, speed. Each specific coordinate represents a feasible solution of the complex problem; the velocity attribute is a geometric vector reflecting the population migration direction and the migration distance. Population velocity updates are constrained by three aspects: (1) the population learning efficiency; (2) relative positions of historical local optimal habitat of the affiliated species and the current population habitat; (3) the relative position of the global optimal habitat and the current population habitat. The population update speed varies with the current population fitness. And each population realizes the search of a solution space in the migration process.
Position coordinate update formula:
Figure RE-GDA0003120068900000081
velocity update formula:
when the population belongs to a global optimal fitness population:
Figure RE-GDA0003120068900000082
when the population only belongs to the population with the optimal fitness of the subordinate species:
Figure RE-GDA0003120068900000083
③ when the population only belongs to the general population:
Figure RE-GDA0003120068900000084
Figure RE-GDA0003120068900000085
in the formula:
Figure RE-GDA0003120068900000086
refers to the velocity vector of the ith population of the species m in the T generation species at the time T;
Figure RE-GDA0003120068900000087
refers to the position coordinate of the ith population T moment of the species m in the T generation species; xglobalbestReferring to historical global optimal fitness population position coordinates; selfXmThe historical optimal fitness population position coordinate of species m is referred to;
Figure RE-GDA0003120068900000088
means species m learning efficiency; d refers to the solution space dimension; n refers to the total number of species; rand means a random number from 0 to 1; w inertial weight, non-negative, adjusts the search range for the solution space.
2) And (3) propagation: the fitness of each population changes after migration. At the moment, species of the same generation all have population with maximum adaptability change rate and global optimal adaptability population. The population with the maximum change rate comprises a population with the maximum fitness rise rate and a population with the maximum fitness decline rate. And breeding the two types of populations to obtain offspring populations, so that the diversity of feasible solutions is increased, and the algorithm is prevented from falling into local optimum.
The adaptability change rate calculation formula is as follows:
Figure RE-GDA0003120068900000091
in the formula:
Figure RE-GDA0003120068900000092
refers to the fitness of the ith population of the T-th generation species at the T moment m;
Figure RE-GDA0003120068900000093
refers to the nth dimension of the position coordinate of the ith population of the species m at the time T in the T generation species.
The population breeding formula with the maximum fitness rise rate is as follows:
Figure RE-GDA00031200689000000914
Figure RE-GDA0003120068900000094
Figure RE-GDA0003120068900000095
Figure RE-GDA0003120068900000096
in the formula: xmax,iRefers to the population X with the maximum fitness rise rate in the T generation speciesT,maxMultiplying the position coordinates of the ith sub-generation population;
Figure RE-GDA0003120068900000097
the nth dimension of the position coordinate of the population with the maximum fitness rise rate in the T generation species is referred to;
Figure RE-GDA0003120068900000098
the maximum population reproduction deviation value of the fitness rise rate in the T generation species is referred to; v. ofmax,iRefers to the population X with the maximum fitness rise rate in the T generation speciesT,maxMultiplying the speed vector of the ith sub-generation population;
Figure RE-GDA0003120068900000099
means the i-th generation seed group velocity inheritance rate;
Figure RE-GDA00031200689000000910
Fitnesssub-max,irespectively refer to the maximum population X of fitness rise rate in the T generation speciesT,maxFitness of (2) and its ith sub-generation population Xmax,iThe fitness of (2); q refers to the number of offspring population propagated by the population with the maximum fitness rise rate; n refers to the total number of species.
Secondly, the population reproduction formula with the maximum fitness decline rate is as follows:
Figure RE-GDA00031200689000000911
Figure RE-GDA00031200689000000912
Figure RE-GDA00031200689000000913
Figure RE-GDA0003120068900000101
in the formula: xmin,iRefers to the population X with the maximum fitness reduction rate in the T generation speciesT,minMultiplying the position coordinates of the ith sub-generation population;
Figure RE-GDA0003120068900000102
refers to the nth dimension of the position coordinate of the population with the maximum fitness decline rate in the T generation species.
Figure RE-GDA0003120068900000103
Refers to the population reproduction deviation amount with the maximum fitness decline rate in the T generation species. v. ofmin,iRefers to the population X with the maximum fitness reduction rate in the T generation speciesT,minVelocity vector for breeding ith progeny population
Figure RE-GDA0003120068900000104
Means the i filial generation population speed inheritance rate;
Figure RE-GDA0003120068900000105
Fitnesssub-min,irespectively refer to the population X with the maximum fitness reduction rate in the T generation speciesT,minFitness of (2) and its ith sub-generation population Xmin,iThe fitness of (2); p refers to the number of offspring population propagated by the population with the maximum fitness decline rate; n refers to the total number of species.
Thirdly, a global optimal fitness population multiplication formula:
Xbest,i＝Xglobalbest+[-Kbesr,Kbest] (5-29)
Figure RE-GDA0003120068900000106
Figure RE-GDA0003120068900000107
Figure RE-GDA0003120068900000108
in the formula: xbest,iRefers to the global optimum fitness population XglobalbestMultiplying the position coordinates of the ith sub-generation population; kbestThe population reproduction deviation amount of the global optimal fitness is referred to; v. ofbest,iRefers to the global optimum fitness population XglobalbestMultiplying the speed vector of the ith sub-generation population;
Figure RE-GDA0003120068900000109
means the i-th generation seed group velocity inheritance rate; fitnessglobal、Fitnesssub-global,iRespectively refer to the fitness of the global optimal fitness population and the ith child population Xbest,iThe fitness of (2); u refers to the number of offspring population bred from the global optimum population(ii) a N refers to the total number of species. .
3) And (3) natural selection: the size of the species to which the population belongs is continuously enlarged after the population is propagated. When the set breeding times threshold G is reached, the information of the historical best habitat is recorded in all the species. And part of populations are eliminated by nature because the current fitness is inferior to the historical optimal fitness of the affiliated species. The information recorded by each species in the historical optimal habitat becomes the initial population of the next algorithm iteration, the feasible solutions to be considered in the solution space are reduced, and the algorithm solving efficiency is improved.
XT+1,m,i＝SelfXT,m (5-33)
vT+1,m,i＝SelfvT,m (5-34)
In the formula: xT+1,m,iThe method comprises the steps of (1) performing algorithm iteration for T +1 times to obtain the position coordinates of a primary population i of a species m; selfXT,mThe position coordinates of the historical optimal habitat of the species m when a reproduction time threshold G is met in the iterative process of the algorithm for T times; v. ofT+1,m,iThe velocity vector of the initial generation population i of the species m in the T +1 algorithm iteration process is referred to; selfvT,mRefers to the velocity vector of the species m with the historical best habitat when the threshold G of the number of reproductions is met in the iteration process of the algorithm for T times.
The intelligent economic dispatching algorithm dispatches output variable processing in the microgrid dispatching optimization: the method is a feasible solution for representing the population coordinates into a complex problem, so the output situation of each moment of the scheduling object is represented by the geographic coordinates of the population. Most scheduling objects are all micro-grid controllable micro-sources, so that the actual output condition of the micro-sources is represented by continuous real numbers.
X＝[Y|J|V]＝[Y1,Y2,...,Yi|J1,J2,...Jj|V1,V2,...Vk]
In the formula: y isiRepresenting the actual output value of the scheduling object Y in the ith scheduling period; j. the design is a squarejRepresenting the actual output value of the scheduling object J in the jth scheduling period; vkRepresenting the actual output value of the scheduling object V in the kth scheduling time interval; i. j and k are differentA scheduling period. The dimension D of the X set is increased as the number of scheduled objects or scheduled time periods is increased, and D is i + j + k.
And (3) adaptability selection: the fitness is used for judging the evaluation of the species group on the habitat and representing the pair advantages and disadvantages of the feasible solution. Different evaluation requirements are different in fitness function, and the target function (4-1) is selected as the fitness function of the intelligent economic dispatching algorithm, namely the actual output of the micro source with the lowest comprehensive operation cost is obtained.
Population velocity vector: the population velocity vector is a power source for the feasible solution to approach to the better solution and finally converge in the iterative process. The magnitude of the population velocity directly affects the efficiency of the algorithm and the accuracy of the solution. According to the method, comprehensive consideration is given, and the selection of the population initial speed is determined by the maximum output power and the minimum output power of each micro-grid controllable unit.
Vm,n＝[vY|vJ|vV]＝[vY,1,vY,2,...,vY,i|vJ,1,vJ,2,...vJ,j|vV,1,vV,2,...vV,k]
vJ,j＝rand*[Ymax-Ymin]
vJ,j＝rand*[Jmax-Jmin]
vV,k＝rand*[Vmax-Vmin]
In the formula: vm,iRefers to the velocity vector of the nth population of species m; v. ofY,iRepresenting the speed of the scheduling object Y in the ith dimension; v. ofJ,jRepresenting the output speed of the scheduling object J in the ith dimension; v. ofV,kRepresenting the speed of the scheduling object V in the ith dimension; y ismax、YminRespectively representing the maximum output power and the minimum output power of a scheduling object Y; j. the design is a squaremax、JminRespectively representing the maximum output power and the minimum output power of a scheduling object J; vmax、VminRespectively represent the maximum output power and the minimum output power of the scheduling object V.
Analysis by calculation example: in order to verify the feasibility of the method, the same multi-microgrid system model is subjected to simulation test. A multi-microgrid model structure shown in the table 5-1 is adopted, and the model is a grid-connected operation system consisting of three small microgrids. The model scheduling strategy is shown in fig. 3. The specific parameters of the microgrid model equipment are as follows:
1. table 5-2 shows the fuel cell parameters of the microgrid model device:
Figure RE-GDA0003120068900000121
tables 5-2: fuel cell parameters
2. Tables 5-3 show micro gas turbine parameters for the microgrid model device:
Figure RE-GDA0003120068900000122
tables 5 to 3: micro gas turbine parameters
3. Tables 5-4 show the energy storage battery parameters of the microgrid model device
Figure RE-GDA0003120068900000123
Tables 5 to 4: energy storage battery parameters
4. Tables 5-5 show peak-to-valley electricity prices for the microgrid model devices:
Figure RE-GDA0003120068900000124
tables 5 to 5: peak-to-valley electricity price
The intelligent economic dispatching algorithm, the basic particle swarm algorithm and the improved particle swarm algorithm are simulated under the same micro-grid system model, and the comprehensive operation cost is transversely compared. Initializing parameters of an intelligent economic dispatching algorithm: the species scale N is 5, and the maximum population reproduction number Q of the rising rate is 5; the population reproduction number P with the maximum fitness decline rate is 5; the number U of the global optimal population breeding is 5; the maximum number of breeding times G is 3; the inertia weight Wmax is 0.8. The basic particle swarm optimization and the improved particle swarm optimization set the particle swarm Size to be 500, the maximum inertia factor Wmax to be 0.9, the minimum inertia factor Wmin to be 0.4, and the learning factor C1 to be C2 to be 1.49. The three optimization algorithms are independently calculated for 20 times, the iteration upper limit times are 300, and the optimal calculation result is obtained.
The micro-source output in the calculation example is optimized through an intelligent economic dispatching algorithm, and the results are shown in fig. 4, 5 and 6. Meanwhile, the optimization results of the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm on the same example are compared transversely, and are shown in tables 5-6 and fig. 7.
Intelligent economic dispatching algorithm Basic particle swarm algorithm Improved particle swarm algorithm
3711 Yuan 3752 Yuan tea 3740 yuan
TABLE 5-6 comparison of the cost of operation of the three algorithms
As can be seen from tables 5-6 and FIG. 7, the intelligent economic dispatching algorithm of the invention has the advantages of high convergence rate, high solving precision and effectively reduced comprehensive operation cost. Meanwhile, compared with a common optimization algorithm: the basic particle swarm algorithm and the improved particle swarm algorithm prove that the method has more advantages.
By adopting the technical scheme, the invention has the following advantages: the algorithm is simple in structure, easy to understand, high in convergence speed and high in solving precision, controllable distributed power supply output power in each microgrid can be reasonably planned, and the comprehensive operation cost of the microgrid system is effectively reduced. The method and the device are easy to provide the optimal control scheduling instruction considering the multiple micro-grids on the premise of meeting the load power consumption quality and the safe operation of the micro-grids, and realize that the coordinated scheduling between the multiple micro-grids and the power grid has higher economical efficiency and benefit. The invention is simulated by establishing a power model considering multiple micro-grids, proves the rationality of the intelligent economic dispatching algorithm, and simultaneously compared with a common optimization algorithm: the basic particle swarm algorithm and the improved particle swarm algorithm are compared, and the method is proved to have better superiority and high efficiency.
It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The embodiments and features of the embodiments in the present application may be combined with each other without conflict. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Claims (9)

1. An intelligent economic dispatching method considering multi-microgrid joint dispatching optimization is characterized by comprising the following steps of: the method specifically comprises the following steps:
step 1: taking each microgrid micro-source as a scheduling object, expressing the output condition of each scheduling time interval of the scheduling object by using the group geographic coordinates, and obtaining the actual output condition of the micro-source expressed by continuous real numbers, namely:
X＝[Y|J|V]＝[Y1,Y2,...,Yi|J1,J2,...Jj|V1,V2,...Vk]
in the formula: y isiRepresenting the actual output value of the scheduling object Y in the ith scheduling period; j. the design is a squarejRepresenting the actual output value of the scheduling object J in the jth scheduling period; vkRepresenting the actual output value of the scheduling object V in the kth scheduling time interval; i. j, k represent different scheduling periods;
step 2: selecting an objective function (4-1) as a fitness function, wherein the specific formula of the objective function is as follows:
Figure FDA0003085714640000011
in the formula: t represents the total optimization duration of the model; n represents the number of the model micro-grids;
Figure FDA0003085714640000012
for the cost of electricity purchase;
Figure FDA0003085714640000013
cost for distributed power supply operation;
Figure FDA0003085714640000014
cost of maintenance for distributed power supplies;
Figure FDA0003085714640000015
cost for pollution control;
Figure FDA0003085714640000016
earning for selling electricity;
and step 3: initializing species initial generation population, obtaining population initial speed according to the maximum output power and the minimum output power of each micro-grid controllable unit,
and 4, step 4: calculating the population fitness of each species, recording the current population position and speed, marking the optimal fitness population of each species and the global optimal fitness population, and setting the iteration time t as 1;
and 5: judging whether the iteration times T meet a preset iteration threshold value T or not; if yes, go to step 12; if not, entering step 6;
step 6: judging whether the reproduction times G meet a preset reproduction threshold value G or not; if yes, entering step 11; if not, entering step 7;
and 7: judging whether the current species belongs to a global optimal species; if yes, updating the moving speed and the position parameters of the species according to a position coordinate updating formula (5-17) and a speed updating formula (5-18) of the global optimal fitness population, and executing the step 9; if not, entering step 8;
and 8: judging whether the current species belongs to the optimal fitness population of the corresponding species; if yes, updating the moving speed and the position parameters of the species according to a position coordinate updating formula (5-17) and a speed updating formula (5-19) of the optimal fitness population of the affiliated species, and executing the step 9;
if not, updating the species moving speed and the position parameters according to a position coordinate updating formula (5-17) and a speed updating formula (5-20) of the common population and executing the step 9;
and step 9: calculating the fitness of all species, and recording the current species position and speed; marking the population with the optimal fitness and the population with the maximum fitness change rate;
step 10: calculating the fitness of the overall fitness optimal population and the fitness change rate maximum population to reproduce a new offspring population according to the fitness rise rate maximum population reproduction formula, the fitness decline rate maximum population reproduction formula and the overall fitness optimal population reproduction formula; meanwhile, updating the iteration times t ═ t +1, and the multiplication times g ═ g + 1;
step 11: setting the number g of reproduction times to zero, and taking the optimal fitness population of each species as the initial generation population of the next iteration;
step 12: outputting position parameters and fitness values of the globally optimal individuals;
step 13: and obtaining a scheduling instruction of the controllable distributed power supply based on the optimal parameter and the fitness value of the objective function.
2. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: in step 1, the dimension D of the X set is increased as the number of scheduled objects or scheduled time periods is increased, and D is i + j + k.
3. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: the specific calculation formula of the population initial speed in the step 1 is as follows:
Vm,n＝[vY|vJ|vV]＝[vY,1,vY,2,...,vY,i|vJ,1,vJ,2,...vJ,j|vV,1,vV,2,...vV,k]
vJ,j＝rand*[Ymax-Ymin]
vJ,j＝rand*[Jmax-Jmin]
vV,k＝rand*[Vmax-Vmin]
in the formula: vm,iRefers to the velocity vector of the nth population of species m; v. ofY,iRepresenting the speed of the scheduling object Y in the ith dimension; v. ofJ,jRepresenting the output speed of the scheduling object J in the ith dimension; v. ofV,kRepresenting the speed of the scheduling object V in the ith dimension; y ismax、YminRespectively representing the maximum output power and the minimum output power of a scheduling object Y; j. the design is a squaremax、JminRespectively representing the maximum output power and the minimum output power of a scheduling object J; vmax、VminRespectively represent the maximum output power and the minimum output power of the scheduling object V.
4. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: the position coordinate update formula is:
Figure FDA0003085714640000021
wherein v isT,m,i(t+1)Refers to the velocity vector of the ith population of species m in the T generation species at the time T + 1;
Figure FDA0003085714640000022
refers to the position coordinate of species m in the T generation species at the moment of the ith population T.
5. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: the speed updating formula of the population belonging to the global optimal fitness population is as follows:
Figure FDA0003085714640000034
wherein the content of the first and second substances,
Figure FDA0003085714640000035
refers to the velocity vector of the ith population of the species m in the T generation species at the time T; rand means a random number from 0 to 1; the speed updating formula of the population only belonging to the optimal fitness population of the subordinate species is as follows:
Figure FDA0003085714640000036
wherein the content of the first and second substances,
Figure FDA0003085714640000037
refers to the velocity vector of the ith population of the species m in the T generation species at the time T;
Figure FDA0003085714640000038
refers to the position coordinate of the ith population T moment of the species m in the T generation species; xglobalbestReferring to historical global optimal fitness population position coordinates;
Figure FDA0003085714640000039
means species m learning efficiency; w represents inertia weight, non-negative number, and adjusts the search range of the solution space;
the speed updating formula of the population which only belongs to the common population is as follows:
Figure FDA00030857146400000310
wherein the content of the first and second substances,
Figure FDA00030857146400000311
refers to the velocity vector of the ith population of the species m in the T generation species at the time T;
Figure FDA00030857146400000312
refers to the position coordinate of the ith population T moment of the species m in the T generation species; xglobalbestGlobal optimal fitness population coordinates are referred to; selfXmThe species m is a historical optimal fitness population coordinate;
Figure FDA00030857146400000313
means species m learning efficiency; w inertial weight, non-negative, adjusts the search range for the solution space.
6. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 5, characterized in that: species m learning efficiency
Figure FDA00030857146400000314
The specific calculation formula of (2) is as follows:
Figure FDA0003085714640000031
wherein the content of the first and second substances,
Figure FDA00030857146400000315
referring to the nth dimension of the global optimal fitness population coordinate;
Figure FDA00030857146400000316
the nth dimension of the position coordinate of the historical optimal fitness population of the species m;
Figure FDA00030857146400000317
means species m learning efficiency; d meansSolving the spatial dimension; n refers to the total number of species.
7. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: the constraint conditions of the output power of the controllable distributed power supplies of the multiple micro-grids are as follows:
(1) and (3) a controllable micro-source output power constraint condition:
Figure FDA0003085714640000032
Figure FDA0003085714640000033
Figure FDA0003085714640000041
Figure FDA0003085714640000042
in the formula:
Figure FDA0003085714640000043
respectively representing the actual output power of the fuel cell and the micro gas turbine at the moment t of the ith microgrid;
Figure FDA0003085714640000044
Figure FDA0003085714640000045
representing the upper and lower output limits of the fuel cell stack;
Figure FDA0003085714640000046
representing the upper and lower output limits of the micro gas turbine;
Figure FDA0003085714640000047
Figure FDA0003085714640000048
respectively representing the upper limit of the output power of the fuel cell, the lower limit of the output power of the fuel cell, the upper limit of the output power of the micro gas turbine and the lower limit of the output power of the micro gas turbine;
(2) and (3) charge and discharge constraint conditions of the storage battery energy storage system:
Figure FDA0003085714640000049
SOCmin≤SOCi,t≤SOCmax (5-6)
in the formula:
Figure FDA00030857146400000410
the actual charging and discharging power of the storage battery energy storage system at the ith microgrid t moment is represented; SOCi,tThe actual charge quantity of the storage battery energy storage system at the moment t of the ith microgrid is represented;
Figure FDA00030857146400000411
representing the upper and lower limits of the charging and discharging power of the energy storage battery; SOCmax、SOCminRespectively representing the upper limit and the lower limit of the safe charge capacity of the energy storage battery;
(3) when the storage battery energy storage system is charged and discharged, the battery charge at the moment t +1 is expressed as:
Figure FDA00030857146400000412
in the formula:
Figure FDA00030857146400000413
the charging and discharging power of the ith microgrid at the moment t is represented; w represents the total capacity of the accumulator energy storage systemAn amount;
(4) the microgrid system power balance formula is as follows:
Figure FDA00030857146400000414
in the formula:
Figure FDA00030857146400000415
the total power purchased from the ith microgrid to the power grid at the moment t is represented;
Figure FDA00030857146400000416
the total power of electricity sold to the power grid by the ith microgrid at the moment t is represented;
Figure FDA00030857146400000417
the total output power of the ith micro gas turbine of the microgrid at the moment t is represented;
Figure FDA00030857146400000418
the total output power of the ith micro-grid fuel cell at the moment t is represented;
Figure FDA00030857146400000419
the total power of the wind power generation of the ith microgrid at the moment t is represented;
Figure FDA00030857146400000420
the total power of the ith microgrid photovoltaic power generation at the time t is represented;
Figure FDA00030857146400000421
and the total charging and discharging power of the ith microgrid energy storage system at the moment t is represented.
8. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization according to claim 1, characterized in that: the calculation steps of the objective function, namely the calculation formula with the lowest comprehensive operation cost are as follows;
(1) the operation cost of the micro-grid system consists of all micro-grid controllable distributed power supplies, the operation cost comprises the operation cost of a fuel cell and a micro gas turbine, and the following formula is shown:
Figure FDA0003085714640000051
Figure FDA0003085714640000052
Figure FDA0003085714640000053
in the formula:
Figure FDA0003085714640000054
n represents the power generation operation cost of the ith microgrid at the moment t; cng、
Figure FDA0003085714640000055
ηfc、ηmtRespectively representing the fuel cost coefficient of the fuel cell, the fuel cost coefficient of the micro gas turbine, the power generation efficiency of the fuel cell and the power generation efficiency of the micro gas turbine; l isHVGNWhich represents the low heating value of the natural gas,
(2) the pollution control cost comprises the pollution control cost of the micro gas turbine and the fuel cell, and is shown by the following formula:
Figure FDA0003085714640000056
in the formula: j is the type number of the pollutant, and is respectively CO2、NOx、CO；
Figure FDA0003085714640000057
Representing ith microgrid tThe cost of pollution control at all times;
Figure FDA0003085714640000058
respectively representing the discharge coefficients of different pollutants and the pollution control cost coefficients of different pollutants;
(3) the maintenance cost comprises the maintenance cost of the distributed power supply and the loss cost of the energy storage battery:
Figure FDA0003085714640000059
in the formula:
Figure FDA00030857146400000510
the maintenance cost of the ith microgrid at the moment t is represented; sigmawt、σpv、σfc、σmt、σbtRepresenting the maintenance cost of a wind turbine generator, a photovoltaic array, a fuel cell, a micro gas turbine and an energy storage battery;
Figure FDA00030857146400000511
respectively representing wind-solar predicted power and energy storage charge and discharge power;
(4) energy transmission between each micro-grid and the power grid according to the existence of a scheduling strategy comprises electricity purchasing cost and electricity selling income, and the specific formula is as follows:
Figure FDA00030857146400000512
Figure FDA00030857146400000513
in the formula:
Figure FDA00030857146400000514
respectively showing the actual electricity purchasing price and the electricity selling price at the time t;
Figure FDA00030857146400000515
and respectively representing the power purchased and sold from the power grid at the ith microgrid at the moment t.
9. The intelligent economic dispatching method considering multi-microgrid joint dispatching optimization of claim 8, characterized in that:
low heating value L of natural gasHVGNHas a value of 9.78 (kW.h)/m3。
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