CN112990418B - Multi-energy cross-region optimal configuration method based on particle swarm-linear programming algorithm - Google Patents

Multi-energy cross-region optimal configuration method based on particle swarm-linear programming algorithm Download PDF

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CN112990418B
CN112990418B CN201911213264.4A CN201911213264A CN112990418B CN 112990418 B CN112990418 B CN 112990418B CN 201911213264 A CN201911213264 A CN 201911213264A CN 112990418 B CN112990418 B CN 112990418B
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尤培培
周树鹏
许钊
曹昉
王椿璞
李桐
李成仁
高效
赵茜
张超
刘思佳
孙启星
李彦林
赵飞
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North China Electric Power University
State Grid Sichuan Electric Power Co Ltd
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State Grid Sichuan Electric Power Co Ltd
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Abstract

The invention discloses a multi-energy optimization configuration method based on a particle swarm-linear programming algorithm, which comprises the steps of determining relevant data required by calculation; taking the minimum total cost of the globally acquired electric power energy sources as an optimization target, and establishing a multi-energy cross-region optimization configuration model; and solving a model based on a particle swarm-linear programming algorithm, and determining and outputting various resource optimization configuration results. The invention can realize multi-energy coordination and optimization configuration between electric power energy and power generation resources and between various power generation energy in a large cross-region range, and reasonably utilizes the resources under the condition of saving cost.

Description

Multi-energy cross-region optimal configuration method based on particle swarm-linear programming algorithm
Technical Field
The invention relates to a power system planning method, in particular to a multi-energy cross-region optimization configuration method based on a particle swarm-linear programming algorithm.
Background
Along with the expansion of the electric power market, not only is the electric power trade gradually expanded, but also the trans-regional large-scale transmission of various resources is induced, the national operators are wide, the energy supply and demand distribution is unequal and unbalanced, but the problem of reasonable configuration of energy among areas is not considered and reasonably planned, so that the improvement of an energy coordination supply mode is urgently needed in China, the production and the transportation complementation of various energy sources among wider areas are realized, and the multi-resource optimization configuration of a larger range is realized.
In view of the foregoing, there is a need for providing a method for coordinated and optimized configuration of multiple energy sources among multiple power generation energy sources over a wide range across regions.
Disclosure of Invention
In order to solve the technical problems, the technical scheme adopted by the invention is to provide a multi-energy optimal configuration method based on a particle swarm-linear programming algorithm, which comprises the following steps:
Determining relevant data required by calculation;
taking the minimum total cost of the globally acquired electric power energy sources as an optimization target, and establishing a multi-energy cross-region optimization configuration model;
based on a particle swarm-linear programming algorithm solving model, determining and outputting various resource optimization configuration results;
The required relevant data includes: determining the electric energy demand quantity, the initial generating capacity selling price, the price of each generating resource, the available various generating resource quantities in each region, the maximum transmission value of each power line, the maximum generating power value in each region and the maximum transmission line of each generating resource; the power generation resource of the embodiment has transportability, and comprises fossil energy sources such as coal, petroleum, natural gas and the like.
In the above method, the multi-energy source includes an electric energy source and a power generation source.
In the above method, the establishing a multi-energy transregional optimization configuration model specifically includes:
Objective function:
The objective function is that the total cost of globally acquiring the electric power energy is minimum under the condition of meeting the electric power energy demand of each region, namely:
wherein, C M is the global total purchase total cost, P i is the average purchase cost finally formed by the region i, E Di represents the required electric quantity of the region i, and n is the number of the regions in the research range, i.e. i= … … n;
the constraint conditions include: global power supply and demand balance constraint, each regional power generation resource constraint, each regional power generation electric quantity equality constraint, each regional power generation electric quantity inequality constraint, each power line transmission limit constraint, each regional power generation limit constraint and power generation resource transportation limit constraint.
In the above method, the method for solving the model based on the particle swarm-linear programming algorithm, and determining and outputting various resource optimization configuration results comprises the following steps:
S31, unifying various power generation resources; performing equivalent conversion according to the heat value relation between various power generation resources produced in each region and standard coal;
S32, coordinating the proportion of the regional power generation resources and the electric energy transaction;
s33, calculating the double-layer energy optimization ratio of the electric power energy and the power generation resource based on the particle swarm algorithm.
In the above method, the step S33 includes the steps of:
s331, obtaining original data: comprises the steps of acquiring data in the step S1;
s332, starting particle swarm optimization iteration, initializing region particles, randomly generating a plurality of region particles, and randomly generating the speed of each region particle;
S333, calculating the fitness of particles in each region of the particle swarm;
s334, evaluating the fitness of each particle in the regional particle swarm, searching the optimal individual position and the optimal group position of the regional particle, and updating the speed and the position of each regional particle according to the optimal position;
s335, judging whether the convergence condition of the calculation is met, and if not, returning to the step S333; if so, the iteration is ended.
In the above method, the step S333 includes the steps of:
b1, optimizing double-layer joint allocation of electric power energy sources and power generation resources;
And B2, calculating the total global total electricity purchasing cost under the corresponding condition of the particles in each region.
In the above method, S333 includes the steps of:
Aiming at the given electric energy transaction duty ratio of each area, the initial generated electricity price of each area and the generated electricity price corresponding to the generated electricity resource, calculating the electric energy required to be generated by each area, wherein the electric energy comprises the local initial generated electricity and the additional generated electricity;
Determining the demand of the power generation resources of each area according to the generated energy, carrying out power generation resource optimization configuration, determining the average price of the new power generation resources of each area according to the distribution result, determining the new electricity price of the additional generated energy, and determining the purchase level average cost of each area;
Re-optimizing the distribution of the electric power energy and the distribution of the power generation resources according to the latest power generation resource price and the additional power generation energy price, thereby entering an iterative loop until the average power purchase cost of each area is not changed any more, and taking the iterative loop as an iteration termination condition;
The result of optimizing the allocation is determined.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
1. the double-layer optimization model considering inter-regional multi-energy coordination optimization configuration is provided, and the inter-regional coordination optimization of two types of energy sources, namely electric power energy and power generation resources, is achieved.
2. The particle swarm optimization of regional proportion coordination is provided, and proportion optimization calculation between two types of energy sources is performed aiming at double-layer energy optimization of electric power energy and power generation resources.
3. An inner-layer network and outer-layer network optimization calculation technology combining particle swarm and linear programming is adopted, and the optimization configuration is carried out on other power generation resources except electric energy in a transportation network.
The invention has the beneficial effects that:
1. And (5) a result of cross-region optimal configuration of multiple energy sources. The method can obtain the results of cross-regional optimal allocation of various energy sources, including the energy source production quantity of each region and the energy source transmission quantity of each branch.
2. The ratio of the electric power energy source to the power generation resource is optimal. The method can obtain the energy ratio of two channels meeting the electric energy requirement of each region in the global range, wherein the two channels respectively refer to the electric power transaction amount transmitted through the electric power network and the electric energy generated by the power generation resource transmitted through the transportation network.
3. The proportion of various power generation resources is optimal. By using the method, the configuration proportion among various power generation resources which can be physically conveyed can be obtained. The multi-energy coordination and optimization configuration between the electric power energy and the power generation resources and between various power generation energy can be realized in a large cross-region range, and the resources are reasonably utilized under the condition of saving the cost.
Drawings
FIG. 1 is a flow chart provided by the present invention;
FIG. 2 is a general flow chart of a double-layer model of the multi-energy cross-region optimization configuration method provided by the invention.
Fig. 3 is a flow chart of the joint optimization provided by the invention.
Detailed Description
The invention is described in detail below with reference to the detailed description and the accompanying drawings.
As shown in fig. 1, the invention provides a multi-energy cross-region optimization configuration method based on a particle swarm-linear programming algorithm, which comprises the following steps:
S1, determining relevant data required by calculation; determining the electric energy demand of each area, the initial generating capacity, the selling price of the initial generating capacity, the price of each generating resource, the available various generating resource amounts of each area, the transmission maximum value of each power line, the maximum value of generating power of each area and the maximum value of each generating resource transportation line; the power generation resource of the embodiment has transportability, and comprises fossil energy sources such as coal, petroleum, natural gas and the like.
S2, establishing a multi-energy cross-region optimization configuration model by taking the overall minimum cost of acquiring the electric power energy as an optimization target.
S3, solving a model based on a particle swarm-linear programming algorithm, and determining and outputting various resource optimization configuration results; including the production of the corresponding various energy sources (electric energy sources, various power generation resources) in each region and the transmission quantity in each line.
The step S2 specifically includes the following steps:
Under the condition of large-scale cross-regional development of marketization, the electric power market/energy market in a larger range has the characteristic of competitive market, the energy selling and purchasing behaviors of various energy calling areas and calling areas have the characteristic of 'economy', the embodiment takes the lowest cost as a decision principle, the electric network only provides power transmission service, and the capacity of electric power line transmission has restriction on the electric power market; the railway, waterway and land transportation have influence on the energy market.
1. Objective function
The objective function is that under the condition of meeting the power energy requirements of each region, the total cost of globally acquiring the power energy is minimum, namely:
Where C M is the global total purchase cost, P i is the average purchase cost resulting from the area i, E Di represents the required power of the area i, and n is the number of areas in the research range, i.e. i= … … n.
2. Constraint conditions
The electric power energy and the power generation resources required by each region can be obtained through local production and purchasing of two channels from the outside, so the constraint conditions can be considered as follows:
① Global power supply and demand balance constraint, and the difference between the global total power generation amount and the line loss power amount is equal to the total power consumption demand amount:
Wherein E Gi represents the power generation amount of the area i, and comprises the sum of the local power generation resource of the area i and the power generation amount of the power generation resource purchased by the area i from other areas; e Ll represents the line loss capacity of the line l, and m is the number of lines in the network.
② The power generation resource constraint of each region, namely the available resource quantity of each region should not be smaller than the sum of the resource quantity of the region for power generation and the resource quantity transmitted to other regions, and meanwhile, the available resource quantity of each region has the maximum constraint:
wherein R G,ii is the resource amount of the region i for the power generation of the region; r T,ij is the amount of resources that zone i delivers to other zone j; r S,i represents the amount of resources available to region i; when there are a plurality of power generation resources, the constraint is established for the different power generation resources, respectively.
③ The power generation capacity equation constraint of each area, namely the power generated by each area is equal to the sum of the power used by the area i and the power transmitted to other areas j:
Wherein E G,ii is a part for the present region in the power generation amount of region i; e T,ij is the generated energy which is transmitted to other areas j by the area i; e S,i denotes the power generation amount of the region i.
④ The inequality constraint of the generated electricity quantity of each area, namely the generated electricity quantity of each area is not less than the initial generated electricity quantity:
ES,i≥ESI,i(i＝1,2,...,n)
The initial power generation amount E SI,i of the area i mainly considers the power generated by the primary energy which cannot be transported in each area i, such as wind, light, water and power, and the power generated by the primary energy which can be transported can be called as the additional power generation amount of the area i.
⑤ Each power line delivery limit constraint:
RTE,j≤RTE,max(i＝1,2,...,n)
Wherein R TE,j represents the power transmission quantity of the power line j.
⑥ Each region generating power limit constraint:
PWi≤PWi,max(i＝1,2,...,n)
Where PW i represents the generated power of region i.
⑦ The power generation resource transportation limit constraint, that is, for each power generation resource, the amount of power generation resource transported by each transportation channel (line) should be smaller than the transportation limit of that channel:
RTr,j≤RTr,max(j＝1,2,...,m)
Where R Tr,j represents the amount of power generation resource R delivered by line j.
In this embodiment, the analytical solution process in step S3 is as follows:
the essence of the problem of cross-regional optimal configuration of multiple energy sources in the embodiment is a problem of comprehensive allocation of resources, namely, optimal configuration of electric energy, coal, petroleum, gas and the like in different transportation networks, and interconnection and intercommunication among multi-layer energy source structures are involved in terms of calculation scale, so that in order to facilitate calculation, the embodiment simplifies a power generation resource layer to illustrate the analysis process of the embodiment.
In this embodiment, all energy sources are divided into two types: the energy source is electric energy, namely energy source conveyed on an electric power network in the form of electric energy, and the energy source is various transportable power generation energy sources, namely power generation resources, wherein the power generation resources have transportability and mainly adopt fossil energy sources such as coal, petroleum, natural gas and the like. In this way, the multi-energy distribution can be simplified to two energy distribution levels, and for any region within range, the electric energy can be considered to originate from both channels, namely the amount of electric power trade transmitted through the electric power network and the amount of electric power generated by the power generation resources transmitted through the transportation network.
Therefore, the embodiment converts the multi-energy optimization configuration into the double-layer energy optimization configuration, which can greatly reduce the calculation scale, and in addition, the other advantage of the classification is that the distribution of the electric energy needs to consider nonlinear line loss, while the loss in the distribution process of the power generation resource can be considered as linear, which is also beneficial to the adoption of the calculation method, therefore, the step S3 specifically includes the following steps:
s31, unifying various power generation resources
Considering that the resource quality of different energy sources is different, and the power generation energy is mainly related to the heat value, performing equivalent conversion according to the heat value relation between various power generation resources produced in each region and standard coal; the matrix defining the power generation resource conversion standard coal coefficients is as follows:
In the formula, the element mu ri of the M matrix is the standard coal conversion coefficient of the power generation resource r produced in the area i, and represents the heat generated by one unit (for example, kilogram) of the power generation resource r produced in the area i, and corresponds to the standard coal amount for generating the same heat.
Through the above relation, all transportable power generation resources can be integrated into a unified 'integrated resource', so that the problem is reduced to the optimal configuration of electric energy and integrated resources.
S32, coordinating the proportion of the regional power generation resources and the electric energy transaction;
The requirement of the region i, which is met by electric energy transaction, accounts for beta i as the proportion of the total requirement (E Di) of the region, and the combination of beta values of each region is used as the particle position in the proportion optimization link particle swarm algorithm, namely:
B＝(β1,β2,...,βn) (6)
the beta value of each region needs to be established by considering the constraint of global initial power generation, namely the total quantity of power requirements met through power transaction is not smaller than the global total initial power generation quantity.
S33, calculating a double-layer energy optimization ratio of electric power energy and power generation resources based on a particle swarm algorithm;
this step is solved as the outermost model of the overall model of this embodiment (the two-layer model shown in fig. 2), so the overall calculation process is mainly the optimization process of this link. The overall calculation flow is as follows:
in order to distinguish the particles from the particles in the following steps, the particles in the particle swarm algorithm are defined as regional particles.
S331, obtaining original data: including the acquisition of the data in step S1.
S332, starting iteration of a particle swarm algorithm, initializing region particles, randomly generating a plurality of region particles, and randomly generating the speed of each region particle.
S333, calculating the fitness of particles in each region of the particle swarm.
In the embodiment, the fitness of each region particle in the particle swarm is calculated by solving a fitness function of a function in an optimization target, namely the region particle, of a formula (1) corresponding to each region particle; as shown in fig. 2, the method comprises the following steps:
b1, optimizing double-layer joint allocation of electric power energy sources and power generation resources;
And B2, calculating the total global total electricity purchasing cost under the corresponding condition of the particles (region i) in each region.
S334, evaluating the fitness of each regional particle in the particle swarm, searching the optimal individual position and the optimal group position of the regional particle, and updating the speed and the position of each regional particle according to the optimal position.
S335, judging whether the convergence condition of the calculation is met, and if not, returning to the step S333; if so, the iteration is ended.
S336, outputting various resource optimization configuration results.
In this embodiment, step S333 includes the following specific steps:
For each given proportion of the electric energy transaction of each area to the total electric energy demand of the area, the electric energy distribution layer and the resource distribution layer are required to be optimized respectively, the optimization purpose of the electric energy distribution layer is to perform optimized transmission of electric energy on an electric power network, and the resource distribution is required to perform optimized configuration of various power generation resources on a resource transportation layer, so that for optimizing the double-layer combined distribution of electric power energy and power generation resources (step B1), a double-layer combined optimizing model of electric power energy and power generation resource distribution is established, and the electricity purchase cost and the global total electricity purchase cost of each area are further calculated on the calculation result.
As shown in fig. 3, the calculation flow of the double-layer joint allocation of the electric power energy source and the power generation resource specifically includes:
In the double-layer optimization process of the electric power energy source and the power generation resource, firstly, electric energy optimization configuration is carried out, and aiming at the given electric energy transaction duty ratio of each area, the initial generated energy power price of each area and the generated electricity price corresponding to the power generation resource, the electric energy required to be generated in each area can be calculated through the optimal power flow, wherein the electric energy comprises the local initial generated energy and the additional generated energy. The power generation resource demand (formula (13)) of each region can be obtained from the generated energy, then the power generation resource is optimally configured, the average price of the power generation resource of each region, namely the new power generation resource price, is obtained from the distribution result, the new additional generated energy electricity price (formula (14)) is obtained, and meanwhile the purchase level average cost P i of each region can be obtained. And re-optimizing the distribution of the electric power energy and the distribution of the power generation resources by combining the latest power generation resource price and the additional power generation energy price, and entering an iterative loop until the average power purchase cost P i of each area is not changed any more, and taking the iteration termination condition. In the figure, k represents a loop algebra. At this time, the distribution relationship between the two layers of the electric power energy and the power generation resource is balanced, namely, the double-layer distribution of the electric power energy and the power generation resource under a certain fixed electric energy transaction ratio coefficient B achieves the optimal distribution result with the minimum cost. Wherein,
(1) Optimization of power energy distribution layer
After knowing the power trading duty ratio of any area, the various power trading in the electric network only meets the power trading duty ratio of the area, and the residual power demand of the area is compensated by the production of power generation resources. And (3) taking the minimum total cost of all regional power energy purchase as a target, and establishing a power energy distribution model by satisfying ③、⑤、⑥ constraint and tide equation constraint in the constraint conditions, wherein the target function is as follows:
min FE＝min(FE,B+FE,L) (7)
Wherein F E,B represents the electric energy selling cost, namely the total electricity purchasing cost obtained in all power generation areas; f E,L represents the electric power energy transmission cost, i.e., the total cost of electric power transmission obtained on the power transmission side.
In the process of optimizing calculation, according to network parameters, typical power generation power and typical load of each region, taking the minimum global electricity purchasing cost as an optimization target, performing optimal power flow calculation, and then converting various typical powers into optimal electric energy results after obtaining an optimal power flow result, thereby obtaining a final optimization target.
(2) Power generation resource layer allocation based on particle swarm-linear programming
After all parameters of various power generation resources are converted into standard coal, the total amount can be unified and then the distribution optimization can be performed on all the power generation resources, but in consideration of the differences of transportation cost, purchase price, transportation limit value and the like of different resources, the differences still exist after the conversion into the standard coal, so that the distribution of different power generation resources still needs to be distinguished. The embodiment provides internal and external optimization configuration for a power generation resource layer, namely external optimization is specific gravity optimization of various power generation resources, internal optimization is optimization in each power generation resource layer, external optimization is based on a particle swarm optimization, internal optimization is based on a linear programming algorithm, and an objective function is as follows:
The power generation resource allocation layer aims at minimizing the allocation cost of all power generation resources in all areas:
Wherein b represents the type of power generation resource; r represents the r-th power generation resource; f R,B and F R,L represent the sales cost and transportation cost of the power generation resources, respectively, and the concepts are similar to those of F E,B and F E,L in the formula (8).
The objective function of equation (8) needs to satisfy the ①、②、⑦ th constraint. In addition, the power generation resource optimization needs to satisfy: the total demand of the power generation resources of any region i is equal to the sum of the demands of various power generation resources of the region i, namely:
Where R Di represents the total demand of the power generation resources in the region i.
When the particle swarm algorithm is adopted for external optimization, the particle positions correspond to the required amounts of various power generation energy sources in each region, namely:
Where R ri represents the demand of the region i for the power generation resource R. The total demand for power generation resources in each region can be calculated by the formula (13).
The external optimization fitness function is shown in the formula (8), and the calculation of the particle fitness (F R) is based on the sum of the minimum targets respectively reached under the condition of the power generation resource particles based on the distribution cost of various power generation resources.
The process of optimizing each layer inside is completely the same for each power generation resource type, and for the r-th power generation resource, the objective function is as follows:
min(FR,B,r+FR,L,r)(r＝1,2,…,b) (11)
The known condition is n position values (R r1,Rr2,...,Rrn) of the power generation resource R in the power generation resource particle R, and the independent variable combination is the energy quantity of each branch transportation power generation resource R of the network, namely:
RTr＝(RTr,1,RTr,2,...,RTr,m) (12)
Internal optimization needs to satisfy ②、⑦ th constraint.
After the optimization of the inner layer and the outer layer reaches the optimization, the comprehensive result of the optimization of the power generation resource layer is output, namely, the inner layer optimization results of all r power generation resources are overlapped, and various power generation resources are uniformly converted into standard coal, so that the unified result of the optimization of the power generation resource layer can be directly overlapped.
The power generation resource demand of each region is:
R1Di＝((ESSi-EJSi)+EDi×(1-βi))×α (13)
wherein the electric energy converted to the power generation resource demand includes two parts, wherein the first part is obtained from the electric energy optimization result, and represents the additional power generation amount required to supply electric energy to the electric energy transaction (E SSi-EJSi),ESSi represents the generated energy of the region i obtained from the electric energy distribution layer optimization result, and E JSi represents the initial generated energy of the region i;
the second part is the remaining power demand of region i, and the proportion of the total demand (E Di) of region i is (1-beta i).
The relation between the price of the power generation resource and the price of the electric energy (regional additional power generation) generated by the power generation resource is as follows:
PE＝PR×α÷γ (14)
Wherein P E represents the price of the electric energy produced; p R represents the price of the power generation resource; α represents the amount of power generation resources (e.g., kg) required to be consumed per unit power generation (e.g., kilowatt-hour); gamma represents the proportion of fuel cost to electrical energy cost and can be considered a fixed constant for a particular power generation resource class.
The present invention is not limited to the above-described preferred embodiments, and any person who is informed of structural changes made under the teaching of the present invention should fall within the scope of the present invention, regardless of whether the technical solution is the same as or similar to the present invention.

Claims (2)

1. The multi-energy optimal configuration method based on the particle swarm-linear programming algorithm is characterized by comprising the following steps of:
S1, determining relevant data required by calculation; the required relevant data includes: determining the electric energy demand quantity, the initial generating capacity selling price, the price of each generating resource, the available various generating resource quantities in each region, the maximum transmission value of each power line, the maximum generating power value in each region and the maximum transmission line of each generating resource;
S2, taking the overall minimum cost of obtaining the electric power energy as an optimization target, and establishing a multi-energy cross-region optimization configuration model, wherein the multi-energy cross-region optimization configuration model specifically comprises the following steps:
Objective function:
The objective function is that the total cost of globally acquiring the electric power energy is minimum under the condition of meeting the electric power energy demand of each region, namely:
wherein, C M is the global total purchase total cost, P i is the average purchase cost finally formed by the region i, E Di represents the required electric quantity of the region i, and n is the number of the regions in the research range, i.e. i= … … n;
The constraint conditions include: global power supply and demand balance constraint, each regional power generation resource constraint, each regional power generation electric quantity equality constraint, each regional power generation electric quantity inequality constraint, each power line transmission limit constraint, each regional power generation limit constraint and power generation resource transportation limit constraint; the multi-energy source comprises an electric power energy source and a power generation resource;
s3, determining and outputting various resource optimization configuration results based on a particle swarm-linear programming algorithm solution model, wherein the method specifically comprises the following steps of:
S31, unifying various power generation resources; performing equivalent conversion according to the heat value relation between various power generation resources produced in each region and standard coal;
s32, coordinating the proportion of the regional power generation resources and the electric energy transaction; in particular, the method comprises the steps of,
The ratio of the electric energy transaction meeting requirement of the region i to the total requirement (E Di) of the region is beta i, and the combination of beta values of each region is used as the particle position in the optimization proportioning link particle swarm algorithm, namely:
B＝(β1,β2,...,βn)；
The establishment of the beta value of each region needs to consider the constraint of global initial power generation, namely the total quantity of the electric energy demand met through electric energy transaction is not less than the global total initial power generation quantity;
S33, calculating a double-layer energy optimization ratio of electric power energy and power generation resources based on a particle swarm algorithm; wherein,
The step S33 includes the steps of:
s331, obtaining original data: comprises the steps of acquiring data in the step S1;
S332, starting particle swarm algorithm iteration, initializing particles, randomly generating a plurality of region particles, and randomly generating the speed of each region particle;
S333, calculating the fitness of particles in each region of the particle swarm;
S334, evaluating the fitness of each regional particle in the particle swarm, searching the optimal individual position and the optimal group position of the regional particle, and updating the speed and the position of each regional particle according to the optimal position;
S335, judging whether the convergence condition of the calculation is met, and if not, returning to the step S333; if so, ending the iteration;
The step S333 includes the steps of:
b1, optimizing double-layer joint allocation of electric power energy sources and power generation resources;
And B2, calculating the total global total electricity purchasing cost under the corresponding condition of the particles in each region.
2. The method according to claim 1, wherein the optimizing the dual-layer joint allocation of the electric power source-generating resources in the step B1 specifically includes the steps of:
And (3) electric energy optimizing configuration: aiming at a given electric energy transaction ratio beta i of each area and the initial generated electricity price of each area and the generated electricity price corresponding to the generated electricity resource, calculating the electric energy required to be generated by each area, wherein the electric energy comprises the local initial generated electricity and the additional generated electricity; determining the requirement of power generation resources of each region according to the generated energy;
Optimizing and configuring power generation resources: determining the average price of new power generation resources, namely the price of the new power generation resources, of each area according to the distribution result, determining the price of new additional generated power, and determining the purchase level average cost of each area;
Optimization iteration: re-optimizing the distribution of the electric power energy and the distribution of the power generation resources through an electric energy optimizing and configuring step and a power generation resource optimizing and configuring step according to the new power generation resource price and the new additional power generation capacity price, thereby entering an iterative loop until the average power purchase cost of each area is not changed any more, and taking the iterative loop as an iteration termination condition; the result of optimizing the allocation is determined.
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CN114172541B (en) * 2021-12-02 2023-04-07 广东电网有限责任公司 Energy consumption balance control method, device and system for power line communication network

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105389630A (en) * 2015-11-16 2016-03-09 国网天津市电力公司 Urban energy system supply and demand optimization method based on linear dynamic programming algorithm
CN106454920A (en) * 2016-11-02 2017-02-22 北京邮电大学 Resource allocation optimization algorithm based on time delay guarantee in LTE (Long Term Evolution) and D2D (Device-to-Device) hybrid network
CN109447431A (en) * 2018-10-16 2019-03-08 南京工业大学 A kind of distribution network planning method under urban energy internet based on particle swarm algorithm
CN109871989A (en) * 2019-01-29 2019-06-11 国网山西省电力公司吕梁供电公司 A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource
CN110097543A (en) * 2019-04-25 2019-08-06 东北大学 Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network
AU2019101104A4 (en) * 2019-09-25 2019-10-31 Southeast University An optimal dispatching method of multi-region power and gas coupled integrated energy system using tiered gas tariff
CN110503239A (en) * 2019-07-19 2019-11-26 国网山东省电力公司青岛供电公司 A kind of power distribution network Optimization Scheduling and system considering Reactive Power Ancillary Services

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9817375B2 (en) * 2014-02-26 2017-11-14 Board Of Trustees Of The University Of Alabama Systems and methods for modeling energy consumption and creating demand response strategies using learning-based approaches
US20190258970A1 (en) * 2018-02-19 2019-08-22 Ali Ridha Ali Practical Optimization-Free Economic Load Dispatcher Based on Slicing Fuel-Cost Curves of Electric Generating Machines

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105389630A (en) * 2015-11-16 2016-03-09 国网天津市电力公司 Urban energy system supply and demand optimization method based on linear dynamic programming algorithm
CN106454920A (en) * 2016-11-02 2017-02-22 北京邮电大学 Resource allocation optimization algorithm based on time delay guarantee in LTE (Long Term Evolution) and D2D (Device-to-Device) hybrid network
CN109447431A (en) * 2018-10-16 2019-03-08 南京工业大学 A kind of distribution network planning method under urban energy internet based on particle swarm algorithm
CN109871989A (en) * 2019-01-29 2019-06-11 国网山西省电力公司吕梁供电公司 A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource
CN110097543A (en) * 2019-04-25 2019-08-06 东北大学 Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network
CN110503239A (en) * 2019-07-19 2019-11-26 国网山东省电力公司青岛供电公司 A kind of power distribution network Optimization Scheduling and system considering Reactive Power Ancillary Services
AU2019101104A4 (en) * 2019-09-25 2019-10-31 Southeast University An optimal dispatching method of multi-region power and gas coupled integrated energy system using tiered gas tariff

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
K. Rayudu ; G. Yesuratnam ; Mohammad Ali ; A. Jayalaxmi.Optimal reactive power dispatch based on particle swarm optimization and LP technique.《2016 International Conference on Emerging Technological Trends (ICETT)》.2016,全文. *
基于进化算法的离网混合能源系统控制与优化;马卫武;周若于;方松;薛辛培;;建筑热能通风空调(第11期);全文 *
曹昉 ; 李桐 ; 李成仁 ; 王雅婧.跨区域多能源协调分配的市场价格机制设计(一) 粒子群-线性规划的资源双层优化配置模型.《南方电网技术》.2020,全文. *

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