CN105610202B - Multi-agent system based active control method for autonomous AC/DC micro-grid - Google Patents

Multi-agent system based active control method for autonomous AC/DC micro-grid Download PDF

Info

Publication number
CN105610202B
CN105610202B CN201610078467.7A CN201610078467A CN105610202B CN 105610202 B CN105610202 B CN 105610202B CN 201610078467 A CN201610078467 A CN 201610078467A CN 105610202 B CN105610202 B CN 105610202B
Authority
CN
China
Prior art keywords
power
power supply
microgrid
micro
grid
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201610078467.7A
Other languages
Chinese (zh)
Other versions
CN105610202A (en
Inventor
季宇
刘海涛
苏剑
吴红斌
吴鸣
李洋
赵波
孙丽敬
吕志鹏
于辉
李蕊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Original Assignee
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Zhejiang Electric Power Co Ltd, China Electric Power Research Institute Co Ltd CEPRI filed Critical State Grid Corp of China SGCC
Priority to CN201610078467.7A priority Critical patent/CN105610202B/en
Publication of CN105610202A publication Critical patent/CN105610202A/en
Application granted granted Critical
Publication of CN105610202B publication Critical patent/CN105610202B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/382—
    • 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
    • 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
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Power Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides an active control method of an autonomous alternating current-direct current micro-grid based on a multi-agent system, which comprises the following steps: (1) establishing an agent model, constructing an autonomous AC/DC micro-grid multi-agent system, wherein all entity agents communicate with each other to obtain the power value of each agent in the next time period; (2) judging whether the renewable power supply in the microgrid is sufficient for power generation in the next time period; (3) if the renewable power supply is insufficient in power generation, performing economic dispatching on the power difference of the conventional power supply in a smooth short time; (4) and if the power generation of the renewable power supply is sufficient, calculating the power generation utilization rate of each renewable power supply by using a sub-gradient optimization algorithm. The invention solves the problem of convex optimization in the coordination control of the autonomous microgrid and the problem of the coordinated operation of each distributed power supply in the autonomous microgrid, and improves the stability and reliability of the autonomous operation of the microgrid.

Description

Multi-agent system based active control method for autonomous AC/DC micro-grid
Technical Field
The invention relates to a micro-grid active power control method, in particular to an autonomous alternating current-direct current micro-grid active power control method based on a multi-agent system.
Background
The AC/DC micro-grid technology is an emerging technology for coping with the access of a high-permeability distributed power supply and is the development and progress of the conventional AC micro-grid technology. The AC/DC micro-grid gives consideration to the balance of DC load and DC characteristic power supply on the basis of the traditional AC micro-grid, reduces the loss in the energy conversion process, and has better development prospect under the large background of low-carbon economy.
The AC/DC micro-grid containing the high-permeability distributed power supply can fully exert the advantages of local renewable energy sources during autonomous operation, and improve the local power supply capability. When the micro-grid is in an autonomous operation state, the active power supply and demand balance in the micro-grid must be met, and the maximum power tracking control of the distributed power supply can ensure that the generated power of the distributed power supply is maximum. When the total maximum power generation power of the renewable distributed power supply is smaller than the power required by the microgrid, the conventional power supply is required to output power to realize the power supply and demand balance of the alternating current and direct current side of the microgrid. When the total maximum generated power of the renewable distributed power supply is greater than the required power of the microgrid, if the output power of the distributed power supply is not adjusted in time, the power supply and demand in the microgrid can be unbalanced. At this time, the power generation amount of the renewable distributed power supply should be reduced to satisfy the power supply and demand balance inside the microgrid. In the autonomous distributed coordination control of the microgrid, the output characteristics of the renewable distributed power supply change along with the external environment conditions, so that the output power of the renewable distributed power supply has intermittency and randomness, and the objective function of the coordinated optimization control of the microgrid is a non-differentiable convex function.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the active control method of the autonomous alternating current-direct current micro-grid based on the multi-agent system, solves the problem of convex optimization in the coordination control of the autonomous micro-grid and the problem of the coordinated operation of each distributed power supply in the autonomous micro-grid, and improves the stability and reliability of the autonomous operation of the micro-grid.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
an active control method of an autonomous alternating current-direct current micro-grid based on a multi-agent system comprises the following steps:
(1) establishing an agent model, constructing an autonomous AC/DC micro-grid multi-agent system, wherein all entity agents communicate with each other to obtain the power value of each agent in the next time period;
(2) judging whether the renewable power supply in the microgrid is sufficient for power generation in the next time period;
(3) if the renewable power supply is insufficient in power generation, performing economic dispatching on the power difference of the conventional power supply in a smooth short time;
(4) and if the power generation of the renewable power supply is sufficient, calculating the power generation utilization rate of each renewable power supply by using a sub-gradient optimization algorithm.
Preferably, the step (1) comprises the following steps:
step 1-1, modeling a bottom distributed power supply agent controller by adopting a two-layer control method according to the functional requirements of the distributed power supply in the microgrid, wherein the upper layer is an optimization coordination control layer, and the lower layer is a power generation unit control layer; the optimization coordination control layer comprises a communication module, a power measurement and prediction module and a power generation calculation module, and the power generation unit control layer comprises a power generation control module;
step 1-2, according to the functional requirements of the load in the microgrid, a two-layer control method of a load calculation layer and a load control layer is adopted for modeling of a bottom-layer load agent controller; the upper layer is a load calculation layer, and the lower layer is a load control layer; the load calculation layer comprises a communication module, a load measurement and prediction module and a load calculation module; the load control layer comprises a load control module;
1-3, constructing an autonomous microgrid multi-agent system model according to an autonomous microgrid framework;
step 1-4, according to the environmental condition and weather information of the autonomous AC/DC micro-grid, obtaining the predicted value of the maximum output power of each renewable power supply in the next time period
Figure GDA0002727680870000021
And the total active demand P of the autonomous AC/DC micro-grid in the next periodDAnd i is 1,2, …, n and n are the total number of renewable power sources of the micro-grid.
Preferably, in the step (2), the criterion for judging the next time interval is:
A. when in use
Figure GDA0002727680870000022
When the total active output quantity of the renewable power supply in the microgrid is smaller than the total active demand quantity of the microgrid, the power generation shortage of the renewable power supply in the microgrid is represented;
B. when in use
Figure GDA0002727680870000023
When the total active output quantity of the renewable power supply in the microgrid is equal to the total active demand quantity of the microgrid, the requirement balance in the microgrid is met;
C. when in use
Figure GDA0002727680870000024
And when the total active output quantity of the renewable power supply in the microgrid is larger than the total active demand quantity of the microgrid, the renewable power supply in the microgrid generates enough power.
Preferably, the step (3) comprises the following steps:
3-1, establishing a microgrid power generation cost objective function by adopting a day-ahead scheduling model:
Figure GDA0002727680870000025
in the formula, F1Cost of electricity generation for conventional power sources, F2For pollution control cost of the micro-grid, T is the micro-grid operation period, fi *(Pi(t)) the cost of power generation, P, for a conventional power supply ii(t) is the generated power of the conventional power supply i at the moment t, alphaiThe pollution control cost of the unit generated power of a conventional power supply i is shown, wherein i is 1,2, …, m is the total number of the conventional power supplies of the micro-grid;
step 3-2, setting output power constraint conditions of the conventional power supply, storage battery SOC constraint conditions, storage battery maximum charge and discharge power constraint and microgrid power balance constraint conditions;
the output power constraint condition of the conventional power supply is to limit the change interval of the output power of each conventional power supply in the microgrid, namely:
Pi min≤Pi(t)≤Pi max (2)
in the formula, the corner marks "max" and "min" represent the maximum allowable value and the minimum allowable value of the variable, respectively, Pi maxAnd Pi minRespectively representing the upper and lower limits of the output power of a conventional power supply i;
the constraint condition of the SOC of the storage battery is to limit a change interval of the SOC of the storage battery in the micro-grid, namely:
SOCi min≤SOCi(t)≤SOCi max (3)
in the formula, SOCi maxAnd SOCi minRespectively representing the upper and lower limits of the SOC of the energy storage unit i;
constraint of maximum charging power of the storage battery:
Figure GDA0002727680870000035
and (3) constraint of the maximum discharge power of the storage battery:
Figure GDA0002727680870000036
in the formula, PBi.c.max(t) and PBi.d.max(t) represents the maximum charge/discharge power of the battery i at time t, the charge and discharge of the battery take positive and negative values, and PniIs the rated power, η, of the accumulator iciAnd ηdiRespectively the charge-discharge efficiency, delta, of the accumulator iiAnd ECiThe self-discharge rate and the rated capacity of the storage battery i are respectively;
the constraint condition of the micro-grid power balance is that the total active output quantity of each power supply in the micro-grid alternating current sub-network and the micro-grid direct current sub-network is required to be equal to the total active demand quantity of the micro-grid, namely:
Figure GDA0002727680870000037
Figure GDA0002727680870000038
in the formula, Pi(t) the power generated by the conventional power source i at time t,
Figure GDA0002727680870000041
is at t timeThe maximum power generation power of the renewable power source j of the AC sub-network,
Figure GDA0002727680870000042
for a time t DC sub-network renewable power supply j maximum power generation power, PD_AC(t) is the active demand, P, of the AC sub-network at time tD_DC(t) is the active demand of the direct current sub-network at the moment t;
and 3-3, solving a micro-grid power generation cost objective function by adopting a particle swarm algorithm to obtain an optimal output scheme of the distributed power supply in the micro-grid.
Preferably, the step 3-3 comprises the following steps:
step 3-3-1, initializing a particle population: randomly generating the positions and the speeds of n particles in an allowed range, calculating the fitness of each particle as local optimal fitness, comparing the local optimal fitness of the n particles, selecting the optimal fitness which is recorded as global optimal fitness, and recording the particles as global optimal vectors;
step 3-3-2, updating weight factor w and learning factor c1、c2：
Figure GDA0002727680870000043
In the formula, wmax、wminTaking w as the maximum and minimum values of the inertia weight factormax＝0.9，wmin0.4; f is the current fitness value, favgAnd fminRespectively representing the average fitness value and the minimum fitness value of all the current particles;
Figure GDA0002727680870000044
where Iter represents the number of iterations at that time, ItermaxRepresenting the total number of iterations, c1fAnd c1iIs c1Final and initial values of c2fAnd c2iIs c2C is taken as the final value and the initial value of1i＝c2f＝2.5，c1f＝c2i＝0.5；
3-3-3, calculating the objective function to obtain the fitness value of each particle;
3-3-4, updating local optimal fitness and updating local optimal vector;
3-3-5, updating the global optimal fitness and updating the global optimal vector;
3-3-6, updating the position and the speed of each particle;
Figure GDA0002727680870000045
wherein i is 1,2, …, n is the size of the population, d is a constant representing the dimension of the particle,
Figure GDA0002727680870000046
representing the d-dimensional velocity of the ith particle in the kth iteration;
Figure GDA0002727680870000051
representing the d-dimensional position of the particle in the ith particle in the kth iteration; ω represents the inertial weight; c. C1、c2Which represents a factor of learning that is,
Figure GDA0002727680870000052
representing the individual extreme value of d dimension of the particle i in the k iteration;
Figure GDA0002727680870000053
representing a d-dimensional global extreme value of the whole particle swarm in the k iteration;
Figure GDA0002727680870000054
random numbers distributed in (0, 1) interval;
3-3-7, if the iteration times reach the maximum value, stopping searching and outputting a result; otherwise, returning to the step 3-3-2 to continue the iterative computation.
Preferably, the step (4) comprises the following steps:
step 4-1, defining an objective function H (u) of coordinated operation of the autonomous micro-gridi(k) To minimize the supply-demand difference:
Figure GDA0002727680870000055
in the formula ui(k) Generating power utilization rate for the distributed power source i;
renewable power source i power generation utilization rate ui(k +1) iterative calculation formula:
Figure GDA0002727680870000056
in the formula, aij(t) is the communication weight coefficient between power i agent and power j agent in the k iteration, di(k) Calculating an iteration step size, s, for the kth iteration for the sub-gradienti(k) As an objective function H (u)i(k) In u)i(k) A deflection sub-gradient of (u)i(k +1) is the power generation utilization rate of the (k +1) th iteration renewable power source i;
step 4-2, calculating communication weight coefficient a of the kth iteration renewable power source i agent and the renewable power source j agentij(t)：
Figure GDA0002727680870000057
In the formula, ni(k) The total number of renewable power supply agents communicating with the renewable distributed power supply i-agent for the kth iteration; step 4-3, calculating the kth iteration deflection sub-gradient si(k)：
Figure GDA0002727680870000058
In the formula, deltakIn order to be a deflection factor of the optical system,
Figure GDA0002727680870000059
as an objective function H (u)i(k) In u)i(k) A sub-gradient of (d);
Figure GDA0002727680870000061
at point ui(k) Direction of iteration si(k) Is a linear combination of the current sub-gradient and the last iteration direction;
step 4-4, calculating the iteration step length d of the kth iterationi(k)：
Figure GDA0002727680870000062
Wherein r is a constant;
step 4-5, calculating the reference output power of each power supply in the next time period
Figure GDA0002727680870000063
Each agent issues power generation information to complete active power coordination control;
iteration stop conditions of the sub-gradient optimization algorithm are as follows:
Figure GDA0002727680870000064
calculating reference output power of each renewable distributed power source
Figure GDA0002727680870000065
Figure GDA0002727680870000066
And 4-6, generating power information by each power supply agent to each power supply, and generating power by each power supply according to each power supply agent information to finish active power coordination control of the autonomous microgrid.
Compared with the prior art, the invention has the beneficial effects that:
in order to accelerate the optimizing capability and the convergence speed, the invention adopts the self-adaptive weight coefficient to improve the local and global optimizing capability, adopts the method of dynamically adjusting the learning factor, and strengthens the algorithm to search the global range extensively when the optimizing starts and to search the local range accurately when the optimizing is about to finish. For a non-smooth target function in a minimum supply and demand margin model, a sub-gradient optimization algorithm is adopted to solve the problem of convex optimization of coordination control of the autonomous microgrid, the active coordination control of the autonomous microgrid is combined on the basis of the traditional sub-gradient algorithm, the power generation utilization rate and the communication weight coefficient are added, a self-adaptive iteration step length is determined according to the power margin of the autonomous microgrid after each iteration, the iteration step length of the sub-gradient optimization algorithm is in a positive proportional relation with the power margin after each iteration of the sub-gradient optimization algorithm, the search direction and the search step length of each iteration of the sub-gradient optimization algorithm are improved, and the convergence speed of the sub-gradient optimization algorithm is improved; therefore, the problem of convex optimization in the autonomous micro-grid coordination control is solved, the problem of coordinated operation of all distributed power supplies in the autonomous micro-grid is solved, and the stability and reliability of the autonomous operation of the micro-grid are improved.
Drawings
FIG. 1 is a flow chart of an active power control method of an autonomous AC/DC micro-grid based on a multi-agent system according to the present invention,
FIG. 2 is an iterative flow chart based on particle swarm optimization provided by the present invention,
FIG. 3 is an iterative flow chart of the adaptive step size based sub-gradient optimization algorithm provided by the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention provides an active power control method for an autonomous ac/dc microgrid based on a multi-agent system, which comprises the following specific steps:
step 1, establishing an Agent model, constructing an autonomous AC/DC micro-grid multi-Agent system, and enabling entity agents (agents) to communicate with each other to obtain a next-period power value of each Agent;
according to the functional requirements of the distributed power supply in the microgrid, a two-layer control method is adopted to model a bottom distributed power supply agent controller, wherein the upper layer is an optimization coordination control layer, and the lower layer is a power generation unit control layer; the optimization coordination control layer comprises a communication module, a power measurement and prediction module and a power generation calculation module, and the power generation unit control layer comprises a power generation control module. According to the functional requirements of the load in the microgrid, a two-layer control method of a load calculation layer and a load control layer is adopted for modeling of the bottom layer load agent controller; the upper layer is a load calculation layer, and the lower layer is a load control layer; the load calculation layer comprises a communication module, a load measurement and prediction module and a load calculation module; the load control layer includes a load control module. And constructing an autonomous micro-grid multi-agent system model according to the autonomous micro-grid framework.
According to the environmental condition and weather information of the autonomous AC/DC micro-grid, the predicted value of the maximum output power of each renewable power supply in the next time period is obtained
Figure GDA0002727680870000071
And the total active demand P of the autonomous AC/DC micro-grid in the next periodDWherein i is 1,2, …, n, n is the total number of renewable power sources of the microgrid;
step 2, judging whether the renewable power supply in the microgrid is sufficient for power generation in the next time period;
when in use
Figure GDA0002727680870000072
And when the total active output quantity of the renewable power supply in the microgrid is smaller than the total active demand quantity of the microgrid, the renewable power supply in the microgrid generates insufficient power.
When in use
Figure GDA0002727680870000073
And in time, the total active output quantity of the renewable power supply in the microgrid is equal to the total active demand quantity of the microgrid, and the supply and demand balance is met in the microgrid.
When in use
Figure GDA0002727680870000074
Total active output of renewable power supply in micro-gridAnd the total active power demand of the micro-grid is greater than that of the micro-grid, and the renewable power supply in the micro-grid generates sufficient power.
Step 3, if the power generation of the regenerative power supply is insufficient, economically scheduling the power difference of the conventional power supply in a smooth short time;
firstly, a day-ahead scheduling model is adopted to establish a micro-grid power generation cost objective function:
Figure GDA0002727680870000081
in the formula (1), F1Cost of electricity generation for conventional power sources, F2For pollution control cost of the micro-grid, T is the micro-grid operation period, fi *(Pi(t)) the cost of power generation, P, for a conventional power supply ii(t) is the generated power of the conventional power supply i at the moment t, alphaiThe pollution control cost of the unit generated power of a conventional power supply i is shown, wherein i is 1,2, …, m is the total number of the conventional power supplies of the micro-grid.
Setting an output power constraint condition of a conventional power supply, a storage battery SOC constraint condition, a storage battery maximum charge-discharge power constraint and a micro-grid power balance constraint condition;
the output power constraint condition of the conventional power supply is to limit the change interval of the output power of each conventional power supply in the microgrid, namely:
Pi min≤Pi(t)≤Pi max (2)
in the formula (2), the indices "max" and "min" represent the maximum allowable value and the minimum allowable value of the variable, respectively, and Pi maxAnd Pi minRespectively representing the upper and lower limits of the output power of the conventional power supply i.
The constraint condition of the SOC of the storage battery is to limit a change interval of the SOC of the storage battery in the micro-grid, namely:
SOCi min≤SOCi(t)≤SOCi max (3)
in the formula (3), SOCi maxAnd SOCi minRespectively representing the upper and lower limits of the SOC of the energy storage unit i.
The maximum charging power constraint of the storage battery is as follows:
Figure GDA0002727680870000085
and the maximum discharge power of the storage battery is restricted:
Figure GDA0002727680870000086
in the formulae (4) and (5), PBi.c.max(t) and PBi.d.max(t) represents the maximum charge/discharge power of the battery i at time t, the charge and discharge of the battery take positive and negative values, and Pni-nominal power, η, of the accumulator iciAnd ηdiRespectively the charge-discharge efficiency, delta, of the accumulator iiAnd ECiRespectively, the self-discharge rate and the rated capacity of the storage battery i.
The constraint condition of the micro-grid power balance is that the total active output quantity of each power supply in the micro-grid alternating current sub-network and the micro-grid direct current sub-network is required to be equal to the total active demand quantity of the micro-grid, namely:
Figure GDA0002727680870000091
Figure GDA0002727680870000092
in the formulae (6) and (7), Pi(t) the power generated by the conventional power source i at time t,
Figure GDA0002727680870000093
for the ac sub-network renewable power source j maximum generated power at time t,
Figure GDA0002727680870000094
for the time t, the maximum power generation power, P, of the renewable power supply j of the AC sub-networkD_AC(t) is the active demand, P, of the AC sub-network at time tD_DCAnd (t) is the active demand of the direct current sub-network at the moment t.
Solving the objective function of the generation cost of the micro-grid by adopting a particle swarm algorithm to obtain the optimal output scheme of the distributed power supply in the micro-grid, as shown in fig. 2, the method comprises the following specific steps:
1) initializing a population of particles: randomly generating the positions and the speeds of n particles in an allowed range, calculating the fitness of each particle as local optimal fitness, comparing the local optimal fitness of the n particles, selecting the optimal fitness which is recorded as global optimal fitness, and recording the particles as global optimal vectors;
2) updating the weight factor w and the learning factor c1、c2：
Figure GDA0002727680870000095
In the formula (8), wmax、wminTaking w as the maximum and minimum values of the inertia weight factormax＝0.9，wmin0.4; f is the current fitness value, favgAnd fminRespectively, the average fitness value and the minimum fitness value of all the current particles.
Figure GDA0002727680870000096
In equation (9), Iter represents the number of iterations at that time, ItermaxRepresenting the total number of iterations, c1fAnd c1iIs c1Final and initial values of c2fAnd c2iIs c2C is taken as the final value and the initial value of1i＝c2f＝2.5，c1f＝c2i＝0.5。
3) Calculating the objective function to obtain the fitness value of each particle;
4) updating local optimal fitness and updating local optimal vector of the body;
5) updating the global optimal fitness and updating the global optimal vector;
6) updating the position and velocity of each particle;
Figure GDA0002727680870000101
in the formula (10), i is 1,2, …, n is the size of the population, d is a constant representing the dimension of the particle,
Figure GDA0002727680870000102
representing the d-dimensional velocity of the ith particle in the kth iteration;
Figure GDA0002727680870000103
representing the d-dimensional position of the particle in the ith particle in the kth iteration; ω represents the inertial weight; c. C1、c2Which represents a factor of learning that is,
Figure GDA0002727680870000104
representing the individual extreme value of d dimension of the particle i in the k iteration;
Figure GDA0002727680870000105
representing a d-dimensional global extreme value of the whole particle swarm in the k iteration;
Figure GDA0002727680870000106
is a random number distributed in a (0, 1) interval.
7) And if the iteration times reach the maximum value, stopping searching and outputting a result. Otherwise, returning to the step 2 to continue the iterative computation.
Step 4, if the power generation of the renewable power supply is sufficient, calculating the power generation utilization rate of each renewable power supply by using a sub-gradient optimization algorithm;
as shown in fig. 3, the iterative method based on the adaptive step size sub-gradient optimization algorithm includes the following steps:
when the total active output quantity of the renewable power supply is larger than the total active demand quantity of the microgrid, the output of the renewable power supply is distributed by using a subgradient optimization algorithm, and a target function H (u) of the coordinated operation of the autonomous microgrid is definedi(k) To minimize the difference between supply and demand：
Figure GDA0002727680870000107
In the formula (11), ui(k) And generating power utilization rate for the distributed power source i.
Generating utilization rate u of renewable power source ii(k +1) iterative calculation formula:
Figure GDA0002727680870000108
in the formula (12), aij(t) is the communication weight coefficient between power i agent and power j agent in the k iteration, di(k) Calculating an iteration step size, s, for the kth iteration for the sub-gradienti(k) As an objective function H (u)i(k) In u)i(k) A deflection sub-gradient of (u)iAnd (k +1) is the power generation utilization rate of the (k +1) th iteration renewable power source i.
Calculating communication weight coefficient a of a kth iteration renewable power source i agent and renewable power source j agentij(t)：
Figure GDA0002727680870000109
In the formula (13), ni(k) The total number of renewable power agents communicating with the renewable distributed power i-agent for the kth iteration.
Thirdly, calculating the kth iterative deflection sub-gradient si(k)：
Figure GDA0002727680870000111
In the formula (17), δkIn order to be a deflection factor of the optical system,
Figure GDA0002727680870000112
as an objective function H (u)i(k) In u)i(k) A sub-gradient of (a).
Figure GDA0002727680870000113
At point ui(k) Direction of iteration si(k) Is a linear combination of the current sub-gradient and the last iteration direction.
Fourthly, calculating the iteration step length d of the kth iterationi(k)：
Figure GDA0002727680870000114
In the formula (16), r is a constant.
Calculating the reference output power of each power supply in the next period
Figure GDA0002727680870000115
And each agent issues power generation information to complete active power coordination control.
Iteration stop conditions of the sub-gradient optimization algorithm are as follows:
Figure GDA0002727680870000116
calculating reference output power of each renewable distributed power source
Figure GDA0002727680870000117
Figure GDA0002727680870000118
At the moment, each power supply agent sends power generation information to each power supply, and each power supply generates power according to each power supply agent information, so that active power coordination control of the autonomous microgrid is completed.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (4)

1. An active control method of an autonomous alternating current-direct current micro-grid based on a multi-agent system is characterized by comprising the following steps:
(1) establishing an agent model, constructing an autonomous AC/DC micro-grid multi-agent system, wherein all entity agents communicate with each other to obtain the power value of each agent in the next time period;
(2) judging whether the renewable power supply in the microgrid is sufficient for power generation in the next time period;
(3) if the renewable power supply is insufficient in power generation, performing economic dispatching on the power difference of the conventional power supply in a smooth short time;
(4) if the power generation of the renewable power supply is sufficient, calculating the power generation utilization rate of each renewable power supply by using a sub-gradient optimization algorithm;
the step (3) comprises the following steps:
3-1, establishing a microgrid power generation cost objective function by adopting a day-ahead scheduling model:
Figure FDA0002698366680000011
in the formula, F1Cost of electricity generation for conventional power sources, F2For pollution control cost of the micro-grid, T is the micro-grid operation period, fi *(Pi(t)) the cost of power generation, P, for a conventional power supply ii(t) is the generated power of the conventional power supply i at the moment t, alphaiThe pollution control cost of the unit generated power of a conventional power supply i is shown, wherein i is 1,2, …, m is the total number of the conventional power supplies of the micro-grid;
step 3-2, setting output power constraint conditions of the conventional power supply, storage battery SOC constraint conditions, storage battery maximum charge and discharge power constraint and microgrid power balance constraint conditions;
the output power constraint condition of the conventional power supply is to limit the change interval of the output power of each conventional power supply in the microgrid, namely:
Pi min≤Pi(t)≤Pi max (2)
in the formula, the corner marks "max" and "min" represent the maximum allowable value and the minimum allowable value of the variable, respectively, Pi maxAnd Pi minRespectively representing the upper and lower limits of the output power of a conventional power supply i;
the constraint condition of the SOC of the storage battery is to limit a change interval of the SOC of the storage battery in the micro-grid, namely:
Figure FDA0002698366680000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002698366680000024
and
Figure FDA0002698366680000025
respectively representing the upper and lower limits of the SOC of the energy storage unit i;
constraint of maximum charging power of the storage battery:
Figure FDA0002698366680000026
and (3) constraint of the maximum discharge power of the storage battery:
Figure FDA0002698366680000027
in the formula, PBi.c.max(t) and PBi.d.max(t) represents the maximum charge/discharge power of the battery i at time t, the charge and discharge of the battery take positive and negative values, and PniIs the rated power, η, of the accumulator iciAnd ηdiRespectively the charge-discharge efficiency, delta, of the accumulator iiAnd ECiAre respectively provided withThe self-discharge rate and rated capacity of the storage battery i;
the constraint condition of the micro-grid power balance is that the total active output quantity of each power supply in the micro-grid alternating current sub-network and the micro-grid direct current sub-network is required to be equal to the total active demand quantity of the micro-grid, namely:
Figure FDA0002698366680000028
Figure FDA0002698366680000029
in the formula, Pi(t) the power generated by the conventional power source i at time t,
Figure FDA00026983666800000210
for the ac sub-network renewable power source j maximum generated power at time t,
Figure FDA00026983666800000211
for a time t DC sub-network renewable power supply j maximum power generation power, PD_AC(t) is the active demand, P, of the AC sub-network at time tD_DC(t) is the active demand of the direct current sub-network at the moment t;
3-3, solving a micro-grid power generation cost objective function by adopting a particle swarm algorithm to obtain an optimal output scheme of a distributed power supply in the micro-grid;
the step (1) comprises the following steps:
step 1-1, modeling a bottom distributed power supply agent controller by adopting a two-layer control method according to the functional requirements of the distributed power supply in the microgrid, wherein the upper layer is an optimization coordination control layer, and the lower layer is a power generation unit control layer; the optimization coordination control layer comprises a communication module, a power measurement and prediction module and a power generation calculation module, and the power generation unit control layer comprises a power generation control module;
step 1-2, according to the functional requirements of the load in the microgrid, a two-layer control method of a load calculation layer and a load control layer is adopted for modeling of a bottom-layer load agent controller; the upper layer is a load calculation layer, and the lower layer is a load control layer; the load calculation layer comprises a communication module, a load measurement and prediction module and a load calculation module; the load control layer comprises a load control module;
1-3, constructing an autonomous microgrid multi-agent system model according to an autonomous microgrid framework;
step 1-4, according to the environmental condition and weather information of the autonomous AC/DC micro-grid, obtaining the predicted value of the maximum output power of each renewable power supply in the next time period
Figure FDA0002698366680000031
And the total active demand P of the autonomous AC/DC micro-grid in the next periodDAnd i is 1,2, …, n and n are the total number of renewable power sources of the micro-grid.
2. The control method according to claim 1, wherein in the step (2), the criterion for judging the next period is:
A. when in use
Figure FDA0002698366680000041
When the total active output quantity of the renewable power supply in the microgrid is smaller than the total active demand quantity of the microgrid, the power generation shortage of the renewable power supply in the microgrid is represented;
B. when in use
Figure FDA0002698366680000042
When the total active output quantity of the renewable power supply in the microgrid is equal to the total active demand quantity of the microgrid, the requirement balance in the microgrid is met;
C. when in use
Figure FDA0002698366680000043
And when the total active output quantity of the renewable power supply in the microgrid is larger than the total active demand quantity of the microgrid, the renewable power supply in the microgrid generates enough power.
3. The control method according to claim 1, wherein the step 3-3 comprises the steps of:
step 3-3-1, initializing a particle population: randomly generating the positions and the speeds of n particles in an allowed range, calculating the fitness of each particle as local optimal fitness, comparing the local optimal fitness of the n particles, selecting the optimal fitness which is recorded as global optimal fitness, and recording the particles as global optimal vectors;
step 3-3-2, updating weight factor w and learning factor c1、c2：
Figure FDA0002698366680000044
In the formula, wmax、wminTaking w as the maximum and minimum values of the inertia weight factormax＝0.9，wmin0.4; f is the current fitness value, favgAnd fminRespectively representing the average fitness value and the minimum fitness value of all the current particles;
Figure FDA0002698366680000045
where Iter represents the number of iterations at that time, ItermaxRepresenting the total number of iterations, c1fAnd c1iIs c1Final and initial values of c2fAnd c2iIs c2C is taken as the final value and the initial value of1i＝c2f＝2.5，c1f＝c2i＝0.5；
3-3-3, calculating the objective function to obtain the fitness value of each particle;
3-3-4, updating local optimal fitness and optimal vector;
3-3-5, updating the global optimal fitness and the optimal vector;
3-3-6, updating the position and the speed of each particle;
Figure FDA0002698366680000051
wherein i is 1,2, …, n is the size of the population, d is a constant representing the dimension of the particle,
Figure FDA0002698366680000052
representing the d-dimensional velocity of the ith particle in the kth iteration;
Figure FDA0002698366680000053
representing the d-dimensional position of the particle in the ith particle in the kth iteration; ω represents the inertial weight; c. C1、c2Which represents a factor of learning that is,
Figure FDA0002698366680000054
representing the individual extreme value of d dimension of the particle i in the k iteration;
Figure FDA0002698366680000055
representing a d-dimensional global extreme value of the whole particle swarm in the k iteration; rand1 k、
Figure FDA0002698366680000056
Random numbers distributed in (0, 1) interval;
3-3-7, if the iteration times reach the maximum value, stopping searching and outputting a result; otherwise, returning to the step 3-3-2 to continue the iterative computation.
4. The control method according to claim 1, wherein the step (4) includes the steps of:
step 4-1, defining an objective function H (u) of coordinated operation of the autonomous micro-gridi(k) To minimize the supply-demand difference:
Figure FDA0002698366680000057
in the formula ui(k) Generating power utilization rate for the distributed power source i;
renewable power source i power generation utilization rate ui(k +1) iterative calculation formula:
Figure FDA0002698366680000061
in the formula, aij(t) is the communication weight coefficient between power i agent and power j agent in the k iteration, di(k) Calculating an iteration step size, s, for the kth iteration for the sub-gradienti(k) As an objective function H (u)i(k) In u)i(k) A deflection sub-gradient of (u)i(k +1) is the power generation utilization rate of the (k +1) th iteration renewable power source i; step 4-2, calculating communication weight coefficient a of the kth iteration renewable power source i agent and the renewable power source j agentij(t)：
Figure FDA0002698366680000062
In the formula, ni(k) The total number of renewable power supply agents communicating with the renewable distributed power supply i-agent for the kth iteration;
step 4-3, calculating the kth iteration deflection sub-gradient si(k)：
Figure FDA0002698366680000063
In the formula, deltakIn order to be a deflection factor of the optical system,
Figure FDA0002698366680000064
as an objective function H (u)i(k) In u)i(k) A sub-gradient of (d);
Figure FDA0002698366680000065
at point ui(k) Direction of iteration si(k) Is a linear combination of the current sub-gradient and the last iteration direction;
step 4-4, calculating the iteration step length d of the kth iterationi(k)：
Figure FDA0002698366680000071
Wherein r is a constant;
step 4-5, calculating the reference output power of each power supply in the next time period
Figure FDA0002698366680000072
Each agent issues power generation information to complete active power coordination control;
iteration stop conditions of the sub-gradient optimization algorithm are as follows:
Figure FDA0002698366680000073
calculating reference output power of each renewable distributed power source
Figure FDA0002698366680000074
Figure FDA0002698366680000075
And 4-6, generating power information by each power supply agent to each power supply, and generating power by each power supply according to each power supply agent information to finish active power coordination control of the autonomous microgrid.
CN201610078467.7A 2016-02-04 2016-02-04 Multi-agent system based active control method for autonomous AC/DC micro-grid Active CN105610202B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610078467.7A CN105610202B (en) 2016-02-04 2016-02-04 Multi-agent system based active control method for autonomous AC/DC micro-grid

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610078467.7A CN105610202B (en) 2016-02-04 2016-02-04 Multi-agent system based active control method for autonomous AC/DC micro-grid

Publications (2)

Publication Number Publication Date
CN105610202A CN105610202A (en) 2016-05-25
CN105610202B true CN105610202B (en) 2021-01-08

Family

ID=55989831

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610078467.7A Active CN105610202B (en) 2016-02-04 2016-02-04 Multi-agent system based active control method for autonomous AC/DC micro-grid

Country Status (1)

Country Link
CN (1) CN105610202B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108400614B (en) * 2018-03-02 2021-07-06 广东工业大学 Micro-grid energy control method and device based on particle swarm algorithm
CN109768584B (en) * 2018-12-24 2020-11-24 深圳供电局有限公司 Micro-grid self-control frequency and pressure regulation method and device based on immune particle swarm optimization
CN110380442A (en) * 2019-05-24 2019-10-25 南京工程学院 Micro-capacitance sensor economic load dispatching method based on multi-level optimization strategy
CN110311388A (en) * 2019-05-28 2019-10-08 广东电网有限责任公司电力调度控制中心 Control method for frequency of virtual plant based on distributed projection subgradient algorithm

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104536304A (en) * 2014-12-31 2015-04-22 南京邮电大学 Electric system load multi-agent control method based on Matlab and Netlogo
CN104638643A (en) * 2015-02-12 2015-05-20 清华大学 Full-distributed secondary frequency modulation method for power system microgrid

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104536304A (en) * 2014-12-31 2015-04-22 南京邮电大学 Electric system load multi-agent control method based on Matlab and Netlogo
CN104638643A (en) * 2015-02-12 2015-05-20 清华大学 Full-distributed secondary frequency modulation method for power system microgrid

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid;Yinliang Xu 等;《IEEE TRANSACTIONS ON POWER SYSTEMS》;20140130;第29卷(第1期);第23页，第25-27页，第29页 *
储能系统的优化建模与控制策略研究;王波;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20140315;第13页，第15页 *
含分布式电源的优化调度;刘媛;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20140515;第17-20页，第23-25页 *
基于多agent技术的微电网能量管理系统研究与实现;陈川川;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20151215;第26-27页，第38-39页 *
改进的次梯度优化算法的收敛性;陈静;《金陵科技学院学报》;20080930;第24卷(第3期);第12-16页 *

Also Published As

Publication number Publication date
CN105610202A (en) 2016-05-25

Similar Documents

Publication Publication Date Title
WO2023065552A1 (en) Method for source-grid-load-storage coordinated scheduling in new energy coupling background
CN105610202B (en) Multi-agent system based active control method for autonomous AC/DC micro-grid
Lü et al. Energy optimization of logistics transport vehicle driven by fuel cell hybrid power system
CN107229987A (en) Hybrid energy-storing proportioning computational methods based on light-preserved system running optimizatin
CN105071389B (en) The alternating current-direct current mixing micro-capacitance sensor optimizing operation method and device of meter and source net load interaction
CN108471143A (en) Micro-grid multi-energy method for optimizing scheduling based on positive and negative feedback particle cluster algorithm
CN111525627A (en) Day-ahead scheduling method for flexible direct-current transmission system with pumped storage and new energy power generation
CN111064214A (en) Power distribution network optimal scheduling method based on electric vehicle two-stage rolling strategy
CN113859018B (en) Hierarchical charge-discharge optimization control method for large-scale electric automobile group
CN113988384A (en) Energy storage capacity optimal configuration method for improving reliability of power distribution network
CN114448044A (en) Bidirectional quick-charging ordered charging and discharging method and system for power changing station
CN116388252B (en) Wind farm energy storage capacity optimal configuration method, system, computer equipment and medium
CN111619391B (en) Electric vehicle ordered charging and discharging method based on cooperative game and dynamic time-of-use electricity price
CN107732945A (en) A kind of energy-storage units optimization method based on simulated annealing particle cluster algorithm
CN107240926A (en) A kind of family energy storage intelligent management method under Spot Price
CN111614110A (en) Receiving-end power grid energy storage optimization configuration method based on improved multi-target particle swarm optimization
CN114865747A (en) Energy storage battery cluster state-of-charge power scheduling online balancing method
CN107846035B (en) Wind-solar storage grid-connected type micro-grid considering charging characteristics of electric automobile
CN113715669A (en) Electric vehicle ordered charging control method, system, equipment and readable storage medium
CN110766240B (en) Layered energy storage configuration method for rapid charging station in different scenes
CN109447233B (en) Electric vehicle charging and discharging scheduling method and system
CN109066823B (en) Alternating current-direct current hybrid micro-grid two-layer optimization method suitable for three-port power electronic transformer
CN111509750A (en) Power grid side energy storage system capacity configuration optimization method
CN106529699A (en) Microgrid planning and design method giving consideration to demand side
CN113690918B (en) Centralized battery energy storage power station frequency modulation control strategy based on dynamic grouping technology

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant