CN113937811A - Optimal scheduling method for multi-energy coupling power distribution system - Google Patents

Optimal scheduling method for multi-energy coupling power distribution system Download PDF

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CN113937811A
CN113937811A CN202111199625.1A CN202111199625A CN113937811A CN 113937811 A CN113937811 A CN 113937811A CN 202111199625 A CN202111199625 A CN 202111199625A CN 113937811 A CN113937811 A CN 113937811A
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power
power distribution
particles
distribution system
capacity
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CN113937811B (en
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黄玉萍
张天任
廖晖
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Guangzhou Institute of Energy Conversion of CAS
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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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses an optimal scheduling method for a multi-energy coupling power distribution system, which adopts a GA-PSO algorithm to predict the power generation power of renewable energy sources and regional loads. The method fully utilizes the mobile load storage characteristics of the electric automobile, schedules the load storage resources of the electric automobile, realizes rapid and efficient fault recovery of the power distribution network, improves the toughness of the power distribution system, and enables the multi-energy coupled power distribution system to run economically and stably under the planning. Meanwhile, a genetic algorithm selection mechanism and a cross mechanism are introduced into a particle swarm algorithm, namely, after each iteration of a PSO algorithm, particles to be crossed are selected with a certain probability and are placed into a hybridization pool, the particles in the hybridization pool are randomly combined and crossed in pairs to generate offspring particles, and the offspring particles are used for replacing parent particles, so that the problem that the traditional PSO algorithm is easy to fall into local optimum is solved, the global search capability is improved, and the optimal scheduling of the multi-energy coupling power distribution system is realized.

Description

Optimal scheduling method for multi-energy coupling power distribution system
Technical Field
The invention relates to the technical field of energy Internet, in particular to an optimal scheduling method for a multi-energy coupling power distribution system.
Background
Natural disasters frequently occurring in recent years and high proportion of new energy installed capacity bring huge challenges to the stability of a power distribution system. For example, in 2021, a 2-month U.S. blackout results in an accumulated cutting load of about 20000MW, which affects about 400 thousands of people and brings huge economic loss. In order to improve the recovery capability of a power distribution network under extreme conditions such as natural disasters, network attacks, supply end and line faults and the like, large-scale flexible electric automobile mobile energy storage units are introduced to provide supply and demand balance for an electric-thermal power distribution system. By improving the dispatching method of the multi-energy coupling power distribution system, the power supply requirement of key loads in the system can be met, and even power can be provided for an adjacent power distribution network system.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the present invention provides an optimal scheduling method for a multi-energy coupling power distribution system.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-energy coupling power distribution system optimal scheduling method adopts a GA-PSO algorithm to predict the power generation power and the regional load of renewable energy sources, and comprises the following steps:
step 1: inputting historical data, wherein the historical data comprises historical meteorological data, historical wind power generation data, historical photovoltaic power generation data, historical distribution system load data and historical charging data of electric vehicles, and predicting wind power generation power, photovoltaic power generation power and load power in regions.
Step 2: determining algorithm parameters, and randomly generating a population containing M particles; initializing particles: initializing initial values of photovoltaic capacity and fan capacity and heat capacity to be PPV0, PWT0, PKw 0; each particle was given a random velocity: setting the capacity of the wind turbine generator and the step length of the capacity change of the photovoltaic array, and setting the iteration number to be N; setting the maximum consumption rate of the renewable energy source as a target function; wherein M, N are all positive integers,
and step 3: updating the speed and the position of the particles, and calculating the numerical value of the particles;
and 4, step 4: particle crossing and variation calculation, wherein part of particles are selected as a population needing cross genetic calculation, and the cross and variation calculation is carried out on the particles;
and 5: calculating a particle fitness value and a constraint condition, calculating all particle maintenance costs according to a target function, determining the fitness value of the particles according to the particle maintenance costs, and sequencing the particles according to the fitness value; judging whether the iteration times of the particles are finished, if not, turning to the step 3 to continue the execution; if the maximum iteration number is reached, turning to step 6;
step 6: obtaining wind power generation power, photovoltaic power generation power and load power prediction curves in regions, determining the V2G price elasticity coefficient of the electric automobile according to the relation between renewable energy source fluctuation and the charge-discharge capacity of the electric automobile, and obtaining the service price of the electric automobile participating in V2G; performing charge-discharge optimized scheduling on the response service vehicle to obtain a vehicle scheduling scheme and a service price combination scheme;
and 7: and calculating the schedulable capacity of the electric automobile in the region, and ensuring the energy supply and demand balance of the power distribution network.
Further, in step 2, the objective function is maximization of the renewable energy consumption rate:
Figure BDA0003304444210000021
in the formula, deltareThe consumption rate of renewable energy sources; prcFor the actual consumption of the total amount of renewable energy, PgrThe total power generation amount of renewable energy sources.
Further, in step 3, the velocity and position of the particle are updated according to the following formula:
Figure BDA0003304444210000022
Figure BDA0003304444210000023
in the above formula, the first and second carbon atoms are,
Figure BDA0003304444210000024
the kth iteration speed is the particle i (i ═ 1,2, …, M);
Figure BDA0003304444210000025
is the kth iteration position of particle i;
Figure BDA0003304444210000026
the optimal position of the particle i in the k iteration is obtained;
Figure BDA0003304444210000027
the optimal position of the group in the k iteration is obtained; omega is the inertial weight; c. C1And c2Is a learning factor; r is1And r2Is [0-1 ]]A random number in between.
Further, in step 5, the constraint conditions include the low battery expected value EENS and the energy reliability index EIR.
Further, the calculation formula of the expected insufficient electric quantity EENS in the wind-solar hybrid power generation system is as follows
Figure BDA0003304444210000028
In the formula, PiProbability of representing the i-th capacity case; eiThe electric quantity that the load can not be satisfied; n is the number of different capacity categories.
Further, the power supply reliability index EIR is calculated according to the following formula:
Figure BDA0003304444210000031
in the formula, EL,iThe total electric quantity of the load demand in the ith month; wPV,iThe photovoltaic power generation capacity of the ith month; wWT,iThe power generation capacity of the wind power is the ith month.
Further, the step 6 of determining the price elastic coefficient of the electric vehicle V2G according to the relationship between the renewable energy fluctuation and the electric vehicle charging and discharging capacity includes:
the method comprises the steps of obtaining photovoltaic power generation power, wind power generation power, conventional power generation power and conventional load power, calculating a power gap between supply and demand, setting an elastic coefficient of a dispatching price of the electric automobile according to the power gap, sending a V2G service invitation to the electric automobile, obtaining a V2G service invitation result responded by an electric automobile user, and analyzing the SOC state of a vehicle participating in service and historical charging and discharging data of the vehicle;
the vehicle service area, the expected price distribution, the user participation V2G probability, the expected online time period, and the online time duration distribution are obtained.
Further, the charge and discharge optimization scheduling of the service-responding vehicle of step 6 includes:
a, adjusting V2G excitation measure parameters;
b: acquiring the number of current online service vehicles of the electric automobile, the current grid connection time and the user response degree;
c: selecting a system operation optimization strategy for improving the vehicle service response degree and the grid connection duration from an intelligent decision library;
d: and executing a charge and discharge scheduling program of the electric automobile, comparing and analyzing the excitation measures and the corresponding vehicle scheduling scheme, returning to the optimal V2G vehicle charge and discharge scheduling scheme and scheduling capacity, meeting the goal of consuming renewable energy sources by the electric automobile in the region, and balancing energy under a power distribution system.
Further, the V2G incentive measure parameters comprise time-sharing service price and subsidy strategy.
Further, the system operation optimization strategy for improving the vehicle service response degree and the grid connection duration comprises an electric vehicle-charging/discharging pile resource optimization allocation strategy and a user credit screening strategy.
Compared with the prior art, the invention has the beneficial effects that:
the method fully utilizes the mobile load storage characteristics of the electric automobile, schedules the load storage resources of the electric automobile, realizes rapid and efficient fault recovery of the power distribution network, improves the toughness of the power distribution system, and enables the multi-energy coupled power distribution system to run economically and stably under the planning.
Drawings
Fig. 1 is a flowchart of a method for optimizing and scheduling a multi-energy coupling power distribution system according to an embodiment of the present invention;
FIG. 2 is a flow chart of charge and discharge optimization scheduling for responsive service vehicles.
Detailed Description
Example (b):
in order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the following detailed description of the present invention is provided with reference to the accompanying drawings and detailed description. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
For the establishment of the regional overall scheduling model of the multi-energy coupling power distribution system, a multi-energy optimization problem is firstly considered. Therefore, the comprehensive optimization index for a region becomes very important. Therefore, the invention discloses an optimal scheduling method for a multi-energy coupling power distribution system.
As shown in fig. 1, the optimal scheduling method for a multi-energy coupling power distribution system according to this embodiment uses a GA-PSO algorithm to predict the generated power and the regional load of the renewable energy source, and includes the following steps:
step 1: inputting historical data, wherein the historical data comprises historical meteorological data, historical wind power generation data, historical photovoltaic power generation data, historical distribution system load data and historical charging data of electric vehicles, and predicting wind power generation power, photovoltaic power generation power and load power in regions.
Step 2: determining algorithm parameters, and randomly generating a population containing M particles; initializing particles: initializing initial values of photovoltaic capacity and fan capacity and heat capacity to be PPV0, PWT0, PKw 0; each particle was given a random velocity: setting the capacity of the wind turbine generator and the step length of the capacity change of the photovoltaic array, and setting the iteration number to be N; setting the maximum consumption rate of the renewable energy source as a target function; wherein M, N are all positive integers,
and step 3: updating the speed and the position of the particles, and calculating the values of the particles such as photovoltaic power generation power, wind power generation power and the like;
and 4, step 4: particle crossing and variation calculation, wherein part of particles are selected as a population needing cross genetic calculation, and the cross and variation calculation is carried out on the particles;
and 5: calculating a particle fitness value and a constraint condition, calculating all particle maintenance costs according to a target function, determining the fitness value of the particles according to the particle maintenance costs, and sequencing the particles according to the fitness value; judging whether the iteration times of the particles are finished, if not, turning to the step 3 to continue the execution; if the maximum iteration number is reached, turning to step 6;
step 6: obtaining wind power generation power, photovoltaic power generation power and load power prediction curves in regions, determining the V2G price elasticity coefficient of the electric automobile according to the relation between renewable energy source fluctuation and the charge-discharge capacity of the electric automobile, and obtaining the service price of the electric automobile participating in V2G; performing charge-discharge optimized scheduling on the response service vehicle to obtain a vehicle scheduling scheme and a service price combination scheme;
and 7: and calculating the schedulable capacity of the electric automobile in the region, and ensuring the energy supply and demand balance of the power distribution network.
Therefore, the method fully utilizes the mobile load storage characteristics of the electric automobile, schedules the load storage resources of the electric automobile, realizes rapid and efficient fault recovery of the power distribution network, improves the toughness of the power distribution system, and enables the multi-energy coupling power distribution system to run economically and stably under the planning. Meanwhile, a genetic algorithm selection mechanism and a cross mechanism are introduced into a particle swarm algorithm, namely, after each iteration of a PSO algorithm, particles to be crossed are selected with a certain probability and are placed into a hybridization pool, particles in the hybridization pool are randomly combined and crossed pairwise to generate progeny particles, and the progeny particles are used for replacing parent particles, so that the generated progeny particles inherit the advantages of the parent particles, the searching capability among the particles is enhanced, the problem that the traditional PSO algorithm is easy to fall into local optimum is solved, the global searching capability is improved, and the optimal scheduling of the multifunctional coupling power distribution system is realized.
Specifically, in step 2, the objective function is the maximization of the renewable energy consumption rate:
Figure BDA0003304444210000051
in the formula, deltareThe consumption rate of renewable energy sources; prcFor the actual consumption of the total amount of renewable energy, PgrThe total power generation amount of renewable energy sources.
Specifically, in step 3, the velocity and position of the updated particle are as follows:
Figure BDA0003304444210000052
Figure BDA0003304444210000053
in the above formula, the first and second carbon atoms are,
Figure BDA0003304444210000054
the kth iteration speed is the particle i (i ═ 1,2, …, M);
Figure BDA0003304444210000055
is the kth iteration position of particle i;
Figure BDA0003304444210000056
the optimal position of the particle i in the k iteration is obtained;
Figure BDA0003304444210000057
the optimal position of the group in the k iteration is obtained; omega is the inertial weight; c. C1And c2Is a learning factor; r is1And r2Is [0-1 ]]A random number in between.
Specifically, in step 5, the constraint conditions include the low battery expected value EENS and the energy reliability index EIR. The calculation formula of the expected insufficient electric quantity EENS in the wind-solar hybrid power generation system is as follows:
Figure BDA0003304444210000058
in the formula, PiProbability of representing the i-th capacity case; eiThe electric quantity that the load can not be satisfied; n is the number of different capacity categories.
The calculation formula of the power supply reliability index EIR is shown as the following formula:
Figure BDA0003304444210000059
in the formula, EL,iThe total electric quantity of the load demand in the ith month; wPV,iThe photovoltaic power generation capacity of the ith month; wWT,iThe power generation capacity of the wind power is the ith month.
Specifically, the step 6 of determining the price elastic coefficient of the electric Vehicle V2G (Vehicle-to-grid) according to the relationship between the renewable energy fluctuation and the charging and discharging capacity of the electric Vehicle includes:
the method comprises the steps of obtaining photovoltaic power generation power, wind power generation power, conventional power generation power and conventional load power, calculating a power gap between supply and demand, setting an elastic coefficient of a dispatching price of the electric automobile according to the power gap, sending a V2G service invitation to the electric automobile, obtaining a V2G service invitation result responded by an electric automobile user, and analyzing the State of a vehicle (State of Charge, also called residual electric quantity) participating in service and historical charging and discharging data of the vehicle;
the vehicle service area, the expected price distribution, the user participation V2G probability, the expected online time period, and the online time duration distribution are obtained.
Specifically, as shown in fig. 2, the charge and discharge optimization scheduling for the service-responding vehicle in step 6 includes:
a, adjusting V2G excitation measure parameters;
b: acquiring the number of current online service vehicles of the electric automobile, the current grid connection time and the user response degree;
c: selecting a system operation optimization strategy for improving the vehicle service response degree and the grid connection duration from an intelligent decision library;
d: and executing a charge and discharge scheduling program of the electric automobile, comparing and analyzing the excitation measures and the corresponding vehicle scheduling scheme, returning to the optimal V2G vehicle charge and discharge scheduling scheme and scheduling capacity, meeting the goal of consuming renewable energy sources by the electric automobile in the region, and balancing energy under a power distribution system.
Thus, the optimal vehicle dispatching scheme and service price combination can be obtained through the steps.
Specifically, the V2G incentive measure parameters include time-of-use service price and subsidy policy. The system operation optimization strategy for improving the vehicle service response degree and the grid connection duration comprises an electric vehicle-charging/discharging pile resource optimization allocation strategy and a user credit screening strategy.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.

Claims (10)

1. The optimal scheduling method for the multi-energy coupling power distribution system is characterized in that a GA-PSO algorithm is adopted to predict the power generation power and the regional load of the renewable energy sources, and comprises the following steps:
step 1: inputting historical data, wherein the historical data comprises historical meteorological data, historical wind power generation data, historical photovoltaic power generation data, historical load data of a power distribution system and historical charging data of electric vehicles, and predicting wind power generation power, photovoltaic power generation power and load power in a region;
step 2: determining algorithm parameters, and randomly generating a population containing M particles; initializing particles: initializing initial values of photovoltaic capacity and fan capacity and heat capacity to be PPV0, PWT0, PKw 0; each particle was given a random velocity: setting the capacity of the wind turbine generator and the step length of the capacity change of the photovoltaic array, and setting the iteration number to be N; setting the maximum consumption rate of the renewable energy source as a target function; wherein M, N are all positive integers,
and step 3: updating the speed and the position of the particles, and calculating the numerical value of the particles;
and 4, step 4: particle crossing and variation calculation, wherein part of particles are selected as a population needing cross genetic calculation, and the cross and variation calculation is carried out on the particles;
and 5: calculating a particle fitness value and a constraint condition, calculating all particle maintenance costs according to a target function, determining the fitness value of the particles according to the particle maintenance costs, and sequencing the particles according to the fitness value; judging whether the iteration times of the particles are finished, if not, turning to the step 3 to continue the execution; if the maximum iteration number is reached, turning to step 6;
step 6: obtaining wind power generation power, photovoltaic power generation power and load power prediction curves in regions, determining the V2G price elasticity coefficient of the electric automobile according to the relation between renewable energy source fluctuation and the charge-discharge capacity of the electric automobile, and obtaining the service price of the electric automobile participating in V2G; performing charge-discharge optimized scheduling on the response service vehicle to obtain a vehicle scheduling scheme and a service price combination scheme;
and 7: and calculating the schedulable capacity of the electric automobile in the region, and ensuring the energy supply and demand balance of the power distribution network.
2. The optimal scheduling method for the multi-energy coupling power distribution system according to claim 1, wherein in step 2, the objective function is maximization of renewable energy consumption rate:
Figure RE-FDA0003406527220000011
in the formula, deltareThe consumption rate of renewable energy sources; prcFor the actual consumption of the total amount of renewable energy, PgrThe total power generation amount of renewable energy sources.
3. The optimal scheduling method for multi-energy coupling power distribution system according to claim 1, wherein in step 3, the speed and position of the updated particles are according to the following formula:
Figure RE-FDA0003406527220000021
Figure RE-FDA0003406527220000022
in the above formula, the first and second carbon atoms are,
Figure RE-FDA0003406527220000023
the kth iteration speed is the particle i (i ═ 1,2, …, M);
Figure RE-FDA0003406527220000024
is the kth iteration position of particle i;
Figure RE-FDA0003406527220000025
the optimal position of the particle i in the k iteration is obtained;
Figure RE-FDA0003406527220000026
the optimal position of the group in the k iteration is obtained; omega is the inertial weight; c. ChAnd c2Is a learning factor; r ishAnd r2Is [0-1 ]]A random number in between.
4. The optimal scheduling method for multi-energy coupling power distribution system according to claim 1, wherein in step 5, the constraint conditions include an expected energy shortage EENS and an EIR.
5. The optimal scheduling method for the multi-energy coupling power distribution system according to claim 4, wherein the calculation formula of the expected energy shortage EENS in the wind-solar-energy storage complementary power generation system is as follows:
Figure RE-FDA0003406527220000027
in the formula, PiProbability of representing the i-th capacity case; eiThe electric quantity that the load can not be satisfied; n is the number of different capacity categories.
6. The optimal scheduling method for multi-energy coupling power distribution system according to claim 4, wherein the power supply reliability index EIR is calculated by the following formula:
Figure RE-FDA0003406527220000028
in the formula, EL,iThe total electric quantity of the load demand in the ith month; wPV,iThe photovoltaic power generation capacity of the ith month; wWT,iThe power generation capacity of the wind power is the ith month.
7. The optimal scheduling method for the multi-energy coupling power distribution system according to claim 1, wherein the step 6 of determining the price elastic coefficient of the electric vehicle V2G according to the relation between the renewable energy fluctuation and the electric vehicle charging and discharging capacity comprises the following steps:
the method comprises the steps of obtaining photovoltaic power generation power, wind power generation power, conventional power generation power and conventional load power, calculating a power gap between supply and demand, setting an elastic coefficient of a dispatching price of the electric automobile according to the power gap, sending a V2G service invitation to the electric automobile, obtaining a V2G service invitation result responded by an electric automobile user, and analyzing the SOC state of a vehicle participating in service and historical charging and discharging data of the vehicle;
the vehicle service area, the expected price distribution, the user participation V2G probability, the expected online time period, and the online time duration distribution are obtained.
8. The optimal scheduling method for multi-energy coupling power distribution system according to claim 1, wherein the optimal scheduling of charging and discharging of the response service vehicle of step 6 comprises:
a: adjusting the V2G incentive measure parameters;
b: acquiring the number of current online service vehicles of the electric automobile, the current grid connection time and the user response degree;
c: selecting a system operation optimization strategy for improving the vehicle service response degree and the grid connection duration from an intelligent decision library;
d: and executing a charge and discharge scheduling program of the electric automobile, comparing and analyzing the excitation measures and the corresponding vehicle scheduling scheme, returning to the optimal V2G vehicle charge and discharge scheduling scheme and scheduling capacity, meeting the goal of consuming renewable energy sources by the electric automobile in the region, and balancing energy under a power distribution system.
9. The method for optimized dispatching of multi-energy coupled power distribution systems according to claim 8, wherein the V2G incentive measure parameters comprise time-of-use service price, subsidy strategy.
10. The optimal scheduling method of the multi-energy coupling power distribution system according to claim 8, wherein the system operation optimization strategies for improving the vehicle service response degree and the grid connection duration comprise an electric vehicle-charging/discharging pile resource optimal allocation strategy and a user credit screening strategy.
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