CN111859780A - Micro-grid operation optimization method and system - Google Patents
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Abstract
The invention provides a micro-grid operation optimization method and system, which comprises the following steps: collecting parameters of the micro-grid and inputting the parameters into a micro-grid operation optimization model; solving the microgrid operation optimization model by adopting an improved particle swarm algorithm to obtain a microgrid operation optimization scheme; optimizing the operation of the micro-grid according to the operation optimization scheme of the micro-grid; the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the microgrid operation optimization model is established based on the minimized environment cost as an objective function. The technical scheme provided by the invention overcomes the defects of the traditional PSO algorithm, such as low iterative convergence speed, easy falling into local extreme values and the like, high convergence speed and difficult falling into local extreme values, and can better optimize the operation of the microgrid.
Description
Technical Field
The invention belongs to the technical field of micro-grid operation control, and particularly relates to a micro-grid operation optimization method and system.
Background
The rapid development of Distributed Generation (DG) has had some effect in mitigating the increasingly serious energy and environmental problems. A microgrid composed of DG in various power generation forms generally has the characteristics of small environmental impact, high reliability of energy supply, good economic benefit and the like, and is widely concerned.
However, the operation of the micro-grid is environment-friendly and has economic benefits, and the solution of the optimized operation problem is a nonlinear optimization problem with multiple targets and multiple constraint conditions. The traditional optimization algorithm has difficulty in solving the problem of nonlinear optimization of multi-target multi-constraint conditions. With the development of the intelligent optimization algorithm, the intelligent optimization algorithms such as Particle Swarm Optimization (PSO) algorithm, neural network and Genetic Algorithm (GA) have been widely applied to solving the problems, and good effects are obtained. However, each intelligent optimization algorithm has advantages and disadvantages, and finding a proper solving algorithm for the microgrid optimization problem is one of research difficulties.
As one of intelligent optimization algorithms, the particle swarm PSO algorithm is suitable for a multi-objective optimization problem, and has the advantages of few parameters, simple program, easy implementation, and the like, but the conventional PSO algorithm also has some disadvantages, such as slow iterative convergence speed, easy falling into local extrema, and the like, and the micro-grid operation optimization performed by the conventional PSO algorithm is difficult to realize environment friendliness and high economic benefit.
Disclosure of Invention
In order to overcome the defects of the traditional PSO algorithm, the invention provides a microgrid operation optimization method, which is improved by comprising the following steps:
Collecting parameters of the micro-grid and inputting the parameters into a micro-grid operation optimization model;
solving the microgrid operation optimization model by adopting an improved particle swarm algorithm to obtain a microgrid operation optimization scheme;
carrying out operation optimization on the micro-grid according to the micro-grid operation optimization scheme;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the micro-grid operation optimization model is established based on a minimized environment cost as an objective function.
Preferably, the establishing of the microgrid operation optimization model includes:
and establishing a microgrid operation optimization model by taking the minimum sum of the environmental protection conversion cost, the operation cost and the microgrid loss cost as an objective function and taking power balance, power output limit, node voltage upper and lower limits and energy storage unit capacity limit as constraint conditions.
Preferably, the objective function is calculated as follows:
minF＝FC+F2+λPl
wherein minF represents the objective function of minimization, FCRepresents the running cost, F2Representing the environmental protection reduced cost, lambda represents the active network loss cost coefficient, PlRepresenting the loss of the microgrid;
running cost FCIs calculated as follows:
In the formula, N is the total number of power supplies in the microgrid; fi(Pi) Fuel cost for the ith power supply; fOMi(Pi) is the operating maintenance cost of the ith power supply; pi is the output power of the ith power supply;
environmental protection reduced cost F2Is calculated as follows:
wherein M is the number of types of contaminants; alphaj is the conversion coefficient of the j-th pollutant; EFi, j is the unit emission of the j pollutant generated by the i-th micro power source.
Preferably, the method for solving the microgrid operation optimization model by using the improved particle swarm optimization algorithm to obtain the microgrid operation optimization scheme includes:
step a 1: establishing particle groups by respectively corresponding the position components of the particles in each dimension to each microgrid parameter;
step a 2: initializing the positions of the particles in the parameter range of the micro-grid by adopting a chaotic algorithm;
step a 3: adjusting and improving inertia weight in the particle swarm algorithm based on the self-adaptive algorithm, and updating the positions of particles in the particle swarm according to the inertia weight;
step a 4: adopting a simulated annealing algorithm to receive the update of the particle position;
step a 5: judging whether the current particle swarm position solves a microgrid operation optimization model: if yes, ending; otherwise, turning to step a 3;
the microgrid parameters comprise: the method comprises the following steps of power prediction data, load prediction data, micro-grid operation and maintenance cost data, power climbing rate upper limit data of each power generation unit in the micro-grid and voltage of each node of the micro-grid.
Preferably, the initializing the position of each particle in the micro-grid parameter range by using the chaotic algorithm includes:
randomly initializing the position quantity of one particle;
based on the position quantity, a complete chaotic system is adopted to calculate a plurality of random quantities with chaotic characteristics;
mapping each random quantity into a value range of a parameter of the microgrid;
evaluating the random quantity injected into the parameter value range of the microgrid by adopting a fitness function, and selecting the random quantity with the fitness function value larger than a preset threshold value as an initial value of the position of each particle in the improved particle swarm algorithm;
and the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
Preferably, the random quantity is calculated as follows:
u(l+1)＝μu(l)(1-u(l))
wherein u (l) represents the l-th random quantity, u (l +1) represents the l + 1-th random quantity,μfor the control parameter, u (0) is the position quantity.
Preferably, the calculation formula for updating the positions of the particles in the particle group is as follows:
xi(k+1)＝xi(k)+vi(k+1)
vi(k+1)＝ωi(k)vi(k)+c1r(pbest,i(k)-xi(k))+c2r(gbest-xi(k))
in the formula, xi(k +1) denotes the position of the ith particle at the (k +1) th iteration, xi(k) Representing the position of the ith particle at the kth iteration; v. ofi(k +1) denotes the velocity of the ith particle at the (k +1) th iteration, vi(k) Representing the velocity of the ith particle at the kth iteration; omega i(k) Representing the inertial weight of the ith particle at the kth iteration; c. C1Denotes a first learning factor, c2Represents a second learning factor; r is a random number; p is a radical ofbest,i(k) Represents the optimal position of the ith particle at the kth iteration, gbestRepresenting the optimal position of the entire population of particles.
Preferably, the inertia weight ω of the ith particle at the kth iterationi(k) Is calculated as follows:
in the formula, b is an upper limit of the inertia weight value, and a is a lower limit of the inertia weight value; f. ofi(k) Denotes the fitness index, f, of the ith particle at the kth iterationi(k) Is calculated as follows:
wherein c is a fitness constant, fd(k) Representing the global optimal fitness function value of the whole particle swarm during the kth iteration; f. ofid(k) The local optimal fitness function value of the ith particle at the kth iteration is represented;
the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
Preferably, the receiving the update of the particle position by using the simulated annealing algorithm includes:
calculating the change value of the fitness function value of the corresponding particle before and after the updated particle position;
judging whether the variation value is less than 0: if yes, receiving the update of the particle position; otherwise, receiving the update of the particle position according to the preset probability;
And the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
Based on the same inventive concept, the application also provides a micro-grid operation optimization system, which is characterized by comprising: the system comprises a data acquisition module, an optimization scheme module and an operation optimization module;
the data acquisition module is used for acquiring parameters of the micro-grid and inputting the parameters into the micro-grid operation optimization model;
the optimization scheme module is used for solving the micro-grid operation optimization model by adopting an improved particle swarm algorithm to obtain a micro-grid operation optimization scheme;
the operation optimization module is used for optimizing the operation of the micro-grid according to the micro-grid operation optimization scheme;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the micro-grid operation optimization model is established based on a minimized environment cost as an objective function.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a micro-grid operation optimization method and system, which comprises the following steps: collecting parameters of the micro-grid and inputting the parameters into a micro-grid operation optimization model; solving the microgrid operation optimization model by adopting an improved particle swarm algorithm to obtain a microgrid operation optimization scheme; optimizing the operation of the micro-grid according to the operation optimization scheme of the micro-grid; the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the microgrid operation optimization model is established based on the minimized environment cost as an objective function. The technical scheme provided by the invention overcomes the defects of the traditional PSO algorithm, such as low iterative convergence speed, easy falling into local extreme values and the like, high convergence speed and difficult falling into local extreme values, and can better optimize the operation of the microgrid.
Drawings
Fig. 1 is a schematic flow chart of a microgrid operation optimization method provided by the present invention;
FIG. 2 is a schematic view of a structure of a micro-dot network according to the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of a microspot network in accordance with the present invention;
FIG. 4 is a schematic view of photovoltaic and wind power output prediction curves according to the present invention;
FIG. 5 is a graphical illustration of total load and generalized load prediction curves in accordance with the present invention;
FIG. 6 is a schematic diagram of a load prediction curve for each feeder according to the present invention;
FIG. 7 is a graphical illustration of a distributed power fuel cost versus output power relationship in accordance with the present invention;
FIG. 8 is a graph illustrating the cost versus output power of a distributed power supply according to the present invention;
FIG. 9 is a schematic diagram of the output power curves of various power supplies in accordance with the present invention;
fig. 10 is a schematic view of a microgrid operating cost curve according to the present invention;
fig. 11 is a schematic diagram of a basic structure of a microgrid operation optimization system provided by the present invention;
fig. 12 is a schematic structural diagram of a microgrid operation optimization system provided by the present invention in detail.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
a schematic flow diagram of a microgrid operation optimization method provided by the present invention is shown in fig. 1, and includes:
Step 1: collecting parameters of the micro-grid and inputting the parameters into a micro-grid operation optimization model;
step 2: solving the microgrid operation optimization model by adopting an improved particle swarm algorithm to obtain a microgrid operation optimization scheme;
and step 3: optimizing the operation of the micro-grid according to the operation optimization scheme of the micro-grid;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the microgrid operation optimization model is established based on the minimized environment cost as an objective function.
Specifically, for the microgrid structure shown in fig. 2, a microgrid operation optimization method provided by the present application includes:
step 101: and establishing an objective function, and optimally taking operation indexes such as operation cost, environmental benefit and the like as the objective function of optimizing operation of the micro-grid.
The running cost is calculated by the formula
In the formula, FCEconomic cost for the microgrid; n is the total number of power supplies in the microgrid; fi(Pi) Fuel cost for the ith power supply; fOMi(Pi) is the operating maintenance cost of the ith power source (including the battery). Pi is the output power of the ith power supply. Wherein Fi(Pi)、FOMi(Pi) is a function of the corresponding power output Pi.
The environmental protection conversion cost calculation formula is as follows:
Wherein M is the number of types of contaminants; alpha j is the conversion coefficient of j-type pollutants, yuan/kg; EFi,jThe unit emission of j pollutants generated by the ith micro power source is kg/kW.
The objective function is calculated as:
minF＝FC+F2+λPl
in the formula, λ represents the loss cost coefficient of the active network, PlRepresenting the microgrid loss.
Step 102: and establishing constraint conditions of the optimal operation of the microgrid, namely equality constraint and inequality constraint in the operation of the microgrid, wherein the equality constraint is a power balance equation, and the inequality constraint is various limits, such as power output limit, upper and lower limits of node voltage, capacity limit of an energy storage unit and the like.
Step 103: solving the PSO by adopting an improved particle swarm optimization algorithm, namely combining the adaptive weight, the chaotic algorithm and the simulated annealing thought, specifically, initializing the PSO by using a chaotic system, jumping out a local optimal solution by using the simulated annealing thought, and carrying out detailed search by using the adaptive weight.
In step 103, when the chaotic system is used to initialize the PSO, a d-dimensional random quantity u is generated first0D is a dimension adopted in the particle swarm algorithm, and a random quantity u (k +1) ═ mu (k) (1-u (k)) with chaotic characteristics is obtained according to a complete chaotic system u (k +1) ═ mu (k) (1-u (k)) i0(i0 ═ 1,2, …, M1), where μ is a control parameter, typically between 2 and 4. Then u is puti0The mapping to the value range of the micro-grid parameters, the micro-grid parameters comprise active and reactive power output of each power generation unit, active and reactive power of load consumption, upper limit of power ramp rate, operation and maintenance cost, voltage of each node and the like, and the initial position x can be obtainedi0(0) Setting the reciprocal of the target function as a fitness function, and selecting part of random quantity with good fitness, namely the fitness function value is larger than a preset threshold value, as an initial population of the PSO algorithm through fitness function evaluation;
when the improved particle swarm optimization algorithm is adopted, the position and speed are updated as shown in the following formula:
xi(k+1)＝xi(k)+vi(k+1)
vi(k+1)＝ωi(k)vi(k)+c1r(pbest,i(k)-xi(k))+c2r(gbest-xi(k))，
in the formula, xi(k +1) denotes the position of the ith particle at the (k +1) th iteration, xi(k) Representing the position of the ith particle at the kth iteration; v. ofi(k +1) denotes the velocity of the ith particle at the (k +1) th iteration, vi(k) Representing the velocity of the ith particle at the kth iteration; omegai(k) Representing the inertial weight of the ith particle at the kth iteration; c. C1、c2Is a learning factor, is a non-negative constant, and usually takes on the value of (0, 2)]To (c) to (d); r is a random number between (0, 1); i is 1,2, …, M is the number of particles; p is a radical ofbest,i(k) Represents the optimal position of the ith particle at the kth iteration, g bestRepresenting the optimal position of the entire population of particles.
Wherein the adaptive inertial weight ωi(k) Is calculated as follows:
in the formula, b is an upper limit of the inertia weight value, and a is a lower limit of the inertia weight value; f. ofi(k) Denotes the fitness index, f, of the ith particle at the kth iterationi(k) Is calculated as follows:
wherein c is a fitness constant, fd(k) Representing the global optimal fitness function value of the whole particle swarm during the kth iteration; f. ofid(k) The local optimal fitness function value of the ith particle at the kth iteration is shown.
After obtaining the new position, whether the new position is reserved depends on the simulated annealing algorithm, specifically, the change value E of the fitness value F of the two positions before and after updating is calculated. If E <0, accepting the value of the new location; otherwise, determining whether to accept the new position according to a certain probability, wherein the probability value is preset and can be adjusted according to the situation.
Step 104: and optimizing the operation of the micro-grid according to the micro-grid parameters obtained by solving the improved particle swarm algorithm.
Example 2:
the microgrid structure shown in fig. 3 has specific parameters shown in table 1.
TABLE 1 parameters of the distributed power supplies
Take 24h microgrid operation data on a certain day as an example. FIG. 4 is a power prediction curve for wind power and photovoltaic, where PV represents photovoltaic and WT represents wind power; FIG. 5 is a total Load and generalized Load (including total Load after wind and light) prediction curve, in which PV represents photovoltaic, WT represents wind power, and Load represents total Load; fig. 6 is a load prediction curve of each feeder line, and a graph of the relationship between the fuel cost and the output power of the controllable power sources such as the micro-combustion engine MT, the fuel cell FC and the diesel generator DEG is shown in fig. 7. The combined cost of operation (sum of operation and maintenance and fuel cost) of the controllable power source versus output power is shown in fig. 8.
As can be seen from fig. 7 and 8 and table 1: the MT is highest in the power range of 0 to 135kW in fuel cost and comprehensive cost; the fuel cost of the FC is higher than that of the DEG, but the operation and maintenance cost is the lowest, and the comprehensive cost is the lowest; the DEG has the lowest fuel cost but the highest operation and maintenance cost, so the comprehensive cost is intermediate.
Optimization calculation is performed by using the improved PSO algorithm, and the optimization result is shown in fig. 9, and fig. 10 is a cost analysis of each distributed power supply. As can be seen from fig. 9-10, the total running cost 24h after the microgrid is optimized is 2568 yuan.
Table 2 lists the statistics obtained after 10 runs of the optimization:
TABLE 2 optimization results of different algorithms
As can be seen from table 2: the improved PSO algorithm has the advantages that the total cost of optimizing operation of the microgrid is the lowest, the average operation time is the least, and the improved PSO algorithm has better optimizing capability and faster convergence speed.
Example 3:
based on the same inventive concept, the invention also provides a micro-grid operation optimization system, and the principle of solving the technical problems of the devices is similar to that of the micro-grid operation optimization method, so repeated parts are not repeated.
The basic structure of the system is shown in fig. 11, and comprises: the system comprises a data acquisition module, an optimization scheme module and an operation optimization module;
The data acquisition module is used for acquiring parameters of the micro-grid and inputting the parameters into the micro-grid operation optimization model;
the optimization scheme module is used for solving the micro-grid operation optimization model by adopting an improved particle swarm algorithm to obtain a micro-grid operation optimization scheme;
the operation optimization module is used for optimizing the operation of the micro-grid according to the micro-grid operation optimization scheme;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the microgrid operation optimization model is established based on the minimized environment cost as an objective function.
The detailed structure of the microgrid operation optimization system is shown in fig. 12.
The system also includes a modeling module; the modeling module is used for establishing a microgrid operation optimization model by taking the minimum sum of the environmental protection reduced cost, the operation cost and the microgrid loss cost as a target function and taking power balance, power output limit, node voltage upper and lower limits and energy storage unit capacity limit as constraint conditions.
Wherein, the optimization scheme module includes: the device comprises a particle swarm establishing unit, an initializing unit, a position updating unit, a simulated annealing unit and an ending judging unit;
the particle swarm establishing unit is used for establishing particle swarms according to the position components of the particles in all dimensions, which respectively correspond to all microgrid parameters;
The initialization unit is used for initializing the positions of the particles within the parameter range of the micro-grid by adopting a chaotic algorithm;
the position updating unit is used for adjusting and improving the inertia weight in the particle swarm algorithm based on the self-adaptive algorithm and updating the positions of the particles in the particle swarm according to the inertia weight;
the simulated annealing unit is used for adopting a simulated annealing algorithm to receive the update of the particle position;
and the ending judgment unit is used for judging whether the current particle swarm position solves the microgrid operation optimization model: if yes, ending; otherwise, calling a position updating unit;
the microgrid parameters comprise: the method comprises the following steps of power prediction data, load prediction data, micro-grid operation and maintenance cost data, power climbing rate upper limit data of each power generation unit in the micro-grid and voltage of each node of the micro-grid.
Wherein, the initialization unit includes: the device comprises a random position quantum unit, a chaotic random quantum unit, a microgrid parameter mapping subunit and a particle selection subunit;
a random position quantum unit for randomly initializing a position quantity of a particle;
the chaotic stochastic quantum unit is used for calculating a plurality of random quantities with chaotic characteristics by adopting a complete chaotic system on the basis of the position quantity;
The micro-grid parameter mapping subunit is used for mapping each random quantity into a value range of the micro-grid parameter;
the particle selection subunit is used for evaluating the random quantity injected into the parameter value range of the microgrid by adopting a fitness function, and selecting the random quantity with the fitness function value larger than a preset threshold value as an initial value of the position of each particle in the improved particle swarm algorithm;
and the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
Wherein, simulated annealing unit includes: a fitness function value change subunit and an acceptance judgment subunit;
the fitness function value change subunit is used for calculating the change value of the fitness function value of the corresponding particle before and after the update of the particle position;
an acceptance judging subunit operable to judge whether the variation value is less than 0: if yes, receiving the update of the particle position; otherwise, receiving the update of the particle position according to the preset probability;
and the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.
Claims (10)
1. A microgrid operation optimization method is characterized by comprising the following steps:
collecting parameters of the micro-grid and inputting the parameters into a micro-grid operation optimization model;
solving the microgrid operation optimization model by adopting an improved particle swarm algorithm to obtain a microgrid operation optimization scheme;
Carrying out operation optimization on the micro-grid according to the micro-grid operation optimization scheme;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the micro-grid operation optimization model is established based on a minimized environment cost as an objective function.
2. The method of claim 1, wherein the establishing of the microgrid operation optimization model comprises:
and establishing a microgrid operation optimization model by taking the minimum sum of the environmental protection conversion cost, the operation cost and the microgrid loss cost as an objective function and taking power balance, power output limit, node voltage upper and lower limits and energy storage unit capacity limit as constraint conditions.
3. The method of claim 2, wherein the objective function is calculated as follows:
min F＝FC+F2+λPl
wherein minF represents the objective function of minimization, FCRepresents the running cost, F2Representing the environmental protection reduced cost, lambda represents the active network loss cost coefficient, PlRepresenting the loss of the microgrid;
running cost FCIs calculated as follows:
in the formula, N is the total number of power supplies in the microgrid; fi(Pi) Fuel cost for the ith power supply; f OMi(Pi) is the operating maintenance cost of the ith power supply; pi is the output power of the ith power supply;
environmental protection reduced cost F2Is calculated as follows:
wherein M is the number of types of contaminants; alphaj is the conversion coefficient of the j-th pollutant; EFi, j is the unit emission of the j pollutant generated by the i-th micro power source.
4. The method of claim 2, wherein the solving the microgrid operation optimization model using the improved particle swarm optimization algorithm to obtain the microgrid operation optimization scheme comprises:
step a 1: establishing particle groups by respectively corresponding the position components of the particles in each dimension to each microgrid parameter;
step a 2: initializing the positions of the particles in the parameter range of the micro-grid by adopting a chaotic algorithm;
step a 3: adjusting and improving inertia weight in the particle swarm algorithm based on the self-adaptive algorithm, and updating the positions of particles in the particle swarm according to the inertia weight;
step a 4: adopting a simulated annealing algorithm to receive the update of the particle position;
step a 5: judging whether the current particle swarm position solves a microgrid operation optimization model: if yes, ending; otherwise, turning to step a 3;
the microgrid parameters comprise: the method comprises the following steps of power prediction data, load prediction data, micro-grid operation and maintenance cost data, power climbing rate upper limit data of each power generation unit in the micro-grid and voltage of each node of the micro-grid.
5. The method of claim 4, wherein initializing the position of each particle within the parameters of the microgrid using a chaotic algorithm comprises:
randomly initializing the position quantity of one particle;
based on the position quantity, a complete chaotic system is adopted to calculate a plurality of random quantities with chaotic characteristics;
mapping each random quantity into a value range of a parameter of the microgrid;
evaluating the random quantity injected into the parameter value range of the microgrid by adopting a fitness function, and selecting the random quantity with the fitness function value larger than a preset threshold value as an initial value of the position of each particle in the improved particle swarm algorithm;
and the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
6. The method of claim 5, wherein the random quantity is calculated as follows:
u(l+1)＝μu(l)(1-u(l))
wherein u (l) represents the l-th random quantity, u (l +1) represents the l + 1-th random quantity,μfor the control parameter, u (0) is the position quantity.
7. The method of claim 4, wherein the position update of the particles in the population of particles is calculated as follows:
xi(k+1)＝xi(k)+vi(k+1)
vi(k+1)＝ωi(k)vi(k)+c1r(pbest,i(k)-xi(k))+c2r(gbest-xi(k))
in the formula, xi(k +1) denotes the position of the ith particle at the (k +1) th iteration, x i(k) Representing the position of the ith particle at the kth iteration; v. ofi(k +1) denotes the velocity of the ith particle at the (k +1) th iteration, vi(k) Representing the velocity of the ith particle at the kth iteration; omegai(k) Representing the inertial weight of the ith particle at the kth iteration; c. C1Denotes a first learning factor, c2Represents a second learning factor; r is a random number; p is a radical ofbest,i(k) Represents the optimal position of the ith particle at the kth iteration, gbestRepresenting the optimal position of the entire population of particles.
8. The method of claim 7, wherein the inertia weight ω of the ith particle at the kth iterationi(k) Is calculated as follows:
in the formula, b is an upper limit of the inertia weight value, and a is a lower limit of the inertia weight value; f. ofi(k) Denotes the fitness index, f, of the ith particle at the kth iterationi(k) Is calculated as follows:
wherein c is a fitness constant, fd(k) Representing the global optimal fitness function value of the whole particle swarm during the kth iteration; f. ofid(k) The local optimal fitness function value of the ith particle at the kth iteration is represented;
the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
9. The method of claim 4, wherein accepting updates of particle positions using the simulated annealing algorithm comprises:
Calculating the change value of the fitness function value of the corresponding particle before and after the updated particle position;
judging whether the variation value is less than 0: if yes, receiving the update of the particle position; otherwise, receiving the update of the particle position according to the preset probability;
and the fitness function is the reciprocal of an objective function of the microgrid operation optimization model.
10. A microgrid operation optimization system, comprising: the system comprises a data acquisition module, an optimization scheme module and an operation optimization module;
the data acquisition module is used for acquiring parameters of the micro-grid and inputting the parameters into the micro-grid operation optimization model;
the optimization scheme module is used for solving the micro-grid operation optimization model by adopting an improved particle swarm algorithm to obtain a micro-grid operation optimization scheme;
the operation optimization module is used for optimizing the operation of the micro-grid according to the micro-grid operation optimization scheme;
the improved particle swarm algorithm comprises the following steps: self-adaptive inertia weight adjustment, chaos algorithm initialization particle swarm and simulated annealing receiving particle position updating; the micro-grid operation optimization model is established based on a minimized environment cost as an objective function.
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