CN116885795A - Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium - Google Patents

Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium Download PDF

Info

Publication number
CN116885795A
CN116885795A CN202310737372.1A CN202310737372A CN116885795A CN 116885795 A CN116885795 A CN 116885795A CN 202310737372 A CN202310737372 A CN 202310737372A CN 116885795 A CN116885795 A CN 116885795A
Authority
CN
China
Prior art keywords
micro
grid
network load
source network
load storage
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.)
Pending
Application number
CN202310737372.1A
Other languages
Chinese (zh)
Inventor
何玉灵
吴学伟
孙凯
韩志成
王海朋
杜晓东
曾四鸣
赵建利
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.)
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
North China Electric Power University
Original Assignee
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
North China Electric Power University
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 Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd, North China Electric Power University filed Critical Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
Priority to CN202310737372.1A priority Critical patent/CN116885795A/en
Publication of CN116885795A publication Critical patent/CN116885795A/en
Pending legal-status Critical Current

Links

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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Power Engineering (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (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 embodiment of the disclosure provides a micro-grid source network load storage collaborative scheduling method, device and equipment and a storage medium, which are applied to the technical field of micro-grid operation analysis. The method comprises the following steps: acquiring historical operation data of a micro-grid; solving a micro-grid source network load storage collaborative scheduling model according to historical operation data by adopting a war strategy optimization algorithm; generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result; and carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy. In this way, the method can generate the source-network-charge-storage cooperative scheduling strategy of the micro-grid under the cooperative action of the source-network-charge-storage multi-energy elements based on the historical operation data of the micro-grid, and perform source-network-charge-storage cooperative scheduling on the micro-grid on the basis, so that the efficient consumption of renewable energy sources is promoted to the greatest extent, the carbon emission is reduced, and the electricity quality of users is ensured.

Description

Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium
Technical Field
The disclosure relates to the technical field of micro-grid operation analysis, in particular to a micro-grid source-grid load storage collaborative scheduling method, device and equipment and a storage medium.
Background
With the development of a novel power system and the propulsion of a double-carbon target, the high-proportion wind-light penetration grid connection, the original power grid topological structure becomes more complex. The micro-grid is a small-sized source-grid charge storage system combining distributed energy, load and energy storage devices, and is an effective means for absorbing distributed energy such as wind power photovoltaic and reducing carbon emission. Therefore, the research simultaneously considers the micro-grid source and grid load storage collaborative scheduling including new energy and carbon emission, and has important significance for reducing the running cost of the power distribution system and promoting the landing of the double-carbon targets. However, the problem of poor efficiency generally exists in the current collaborative scheduling of the source network load storage of the micro-grid at home and abroad, so how to improve the collaborative scheduling efficiency of the source network load storage of the micro-grid becomes the technical problem to be solved urgently.
Disclosure of Invention
The embodiment of the disclosure provides a micro-grid source network load storage collaborative scheduling method, device and equipment and a storage medium.
In a first aspect, an embodiment of the present disclosure provides a method for collaborative scheduling of source network load storage of a micro-grid, where the method includes:
acquiring historical operation data of a micro-grid;
solving a micro-grid source network load storage collaborative scheduling model according to historical operation data by adopting a war strategy optimization algorithm;
generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result;
and carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy.
In some implementations of the first aspect, the historical operating data includes: line historical operation data, wind power historical output data, photovoltaic historical output data, micro gas turbine historical output data and storage battery historical data.
In some implementations of the first aspect, a war strategy optimization algorithm is adopted to solve a micro-grid source network load storage collaborative scheduling model according to historical operation data, including:
carrying out initial solution on the micro-grid source network load storage collaborative scheduling model according to the historical operation data to obtain an initial solution;
and adopting a war strategy optimization algorithm to continuously and iteratively optimize the initial solution to obtain an optimal solution.
In some implementations of the first aspect, when a war strategy optimization algorithm is employed to iteratively optimize the initial solution, a weak soldier update strategy of the war strategy optimization algorithm is:
randomly updating weak soldiers in the early stage of iterative optimization;
in the middle of iterative optimization, the weak soldiers are gradually moved to the middle of a battlefield while being randomly updated;
in the later stage of iterative optimization, the weak soldier is placed in the middle of a battlefield;
iterative optimization early passRepresenting that iterative optimization metaphase passes +.>Representing the iterative optimization late pass +.>T represents the number of iterations and T represents the total number of iterations.
In some implementations of the first aspect, the micro-grid source network load storage collaborative scheduling model includes: a micro-grid system model and a multi-objective optimization model.
In some implementations of the first aspect, the micro-grid system model includes: photovoltaic power generation model, wind power generation model, micro gas turbine power generation model and storage battery model;
the multi-objective optimization model takes the minimum running cost of the micro-grid system, the minimum running voltage deviation of the micro-grid system and the minimum carbon emission cost of the micro-grid system as objective functions, and takes micro-grid power balance constraint, micro-gas turbine climbing constraint, distributed power supply power constraint, micro-grid and large-grid energy interaction constraint, storage battery charging and discharging constraint and node voltage constraint as constraint conditions.
In a second aspect, an embodiment of the present disclosure provides a micro-grid source network load storage co-scheduling device, including:
the acquisition module is used for acquiring historical operation data of the micro-grid;
the solving module is used for solving the micro-grid source network load storage collaborative scheduling model according to the historical operation data by adopting a war strategy optimization algorithm;
the generation module is used for generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result;
and the scheduling module is used for carrying out source network charge storage collaborative scheduling on the micro-grid according to the micro-grid source network charge storage collaborative scheduling strategy.
In some implementations of the second aspect, the solution module is specifically configured to:
carrying out initial solution on the micro-grid source network load storage collaborative scheduling model according to the historical operation data to obtain an initial solution;
and adopting a war strategy optimization algorithm to continuously and iteratively optimize the initial solution to obtain an optimal solution.
In a third aspect, embodiments of the present disclosure provide an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
In a fourth aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above-described method.
In the embodiment of the disclosure, the source-network-charge-storage multi-energy element cooperative scheduling strategy of the micro-grid can be generated based on historical operation data of the micro-grid under the cooperative action of the source-network-charge-storage multi-energy elements, and the source-network-charge-storage cooperative scheduling is performed on the micro-grid on the basis, so that the efficient consumption of renewable energy sources is promoted to the greatest extent, the carbon emission is reduced, and the electricity quality of users is ensured.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
fig. 1 shows a flowchart of a micro-grid source-grid load storage collaborative scheduling method provided by an embodiment of the present disclosure;
fig. 2 illustrates a block diagram of a micro-grid source-grid load storage collaborative scheduling device according to an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the disclosure, are within the scope of the disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Aiming at the problems in the background art, the embodiment of the disclosure provides a micro-grid source network load storage collaborative scheduling method, device and equipment and a storage medium. Specifically, historical operation data of a micro-grid is obtained; solving a micro-grid source network load storage collaborative scheduling model according to historical operation data by adopting a war strategy optimization algorithm; generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result; and carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy.
Therefore, the method and the device can generate the source-network-charge-storage cooperative scheduling strategy of the micro-grid under the cooperative action of the source-network-charge-storage multi-energy elements based on the historical operation data of the micro-grid, and perform source-network-charge-storage cooperative scheduling on the micro-grid on the basis, so that the efficient consumption of renewable energy sources is promoted to the greatest extent, the carbon emission is reduced, and the electricity quality of users is ensured.
The method, the device and the equipment for collaborative scheduling of the source network load storage of the micro-grid provided by the embodiment of the disclosure are described in detail below through specific embodiments with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a micro-grid source-network load storage collaborative scheduling method provided by an embodiment of the present disclosure, and as shown in fig. 1, the micro-grid source-network load storage collaborative scheduling method 100 may include the following steps:
s110, acquiring historical operation data of the micro-grid.
Wherein the historical operating data may include: line historical operation data, wind power historical output data, photovoltaic historical output data, micro gas turbine historical output data, storage battery historical data and the like.
And S120, solving the micro-grid source network load storage collaborative scheduling model according to the historical operation data by adopting a war strategy optimization algorithm.
The micro-grid source network load storage collaborative scheduling model can comprise: a micro-grid system model and a multi-objective optimization model.
The micro-grid system model may include: photovoltaic power generation model, wind power generation model, miniature gas turbine power generation model, battery model respectively as follows:
(1) Photovoltaic power generation model
The PV output has volatility and randomness under the influence of sun position, latitude and longitude of a power station, altitude, meteorological factors and the like. The maximum output power is:
P Pv,max ＝E max Aη (1)
wherein P is pv,max Maximum output power for PV for the period of time; e (E) max Is the maximum illumination intensity within the time period; a and η are PV array area and photoelectric conversion efficiency, respectively.
From the PV output characteristics, beta distribution is used to model it:
wherein P is pv Is the actual output power of the PV; Γ is a Gamma function; alpha and Beta are the shape parameters of the Beta distribution, respectively.
(2) Wind power generation model
Considering wind energy intermittence and randomness, the basis of WT output power prediction is its cumulative probability distribution. The vast amount of weather data indicates that wind energy randomness can be described using a weibull distribution. The weibull probability density function for wind speed is:
wherein v is the actual wind speed; b is a shape parameter, 1.8 to 2.8 is taken; a is a scale parameter, reflecting the average wind speed in a certain period of time.
The WT's active output as a function of wind speed is as follows:
wherein P is WT Is the active output of the WT; v r 、v e 、v e The wind speed is respectively cut-in, cut-out and rated speed; p (P) e Maximum power output for WT.
(3) Miniature gas turbine power generation model
The miniature gas turbine has high efficiency and less pollution, belongs to a schedulable micro source, and the actual output power is influenced by various factors, such as the low heat value of fuel. The relation between the operation efficiency and the output power is as follows:
wherein eta MT And P MT The operating efficiency and the output power of the micro gas turbine are respectively.
Output P of micro gas turbine MT And operation management cost C MT The relationship between them is as follows:
wherein K is MT.OM Maintaining a cost coefficient for unit operation of the micro gas turbine; t is a scheduling period, taking 24 hours.
(4) Storage battery model
When the new energy generating capacity meets the load demand, the storage battery is used for storing the residual electric quantity and is used as a power supply together with other equipment in the peak period, so that the waste wind and waste light quantity is reduced. The state of charge of the battery for each period is:
wherein S is OC (t) is the state of charge of the battery at time t; s is S OC (t-1) is the state of charge of the storage battery at the time t-1; lambda is a self-discharge coefficient, taking 0.001; p (P) cha Is the charging power; p (P) dis Is the discharge power; Δt is the time period; mu (mu) cha Taking 0.95 for charging efficiency; mu (mu) dis Taking 0.95 for discharging efficiency; c (C) t The capacity of the battery at time t.
The multi-objective optimization model takes the minimum running cost of the micro-grid system, the minimum running voltage deviation of the micro-grid system and the minimum carbon emission cost of the micro-grid system as objective functions, and takes micro-grid power balance constraint, micro-gas turbine climbing constraint, distributed power supply power constraint, micro-grid and large-grid energy interaction constraint, storage battery charging and discharging constraint and node voltage constraint as constraint conditions.
The minimization of the operating cost of the micro-grid system, the minimization of the operating voltage deviation of the micro-grid system and the minimization of the carbon emission cost of the micro-grid system can be respectively as follows:
(1) Micro grid system operation cost minimization
The method comprises the following steps of minimizing the running cost of a micro-grid system as an objective function, wherein the objective function comprises fuel cost, running cost of each unit of the micro-grid, green certificate-carbon transaction cost, micro-grid and large-grid electric quantity transaction cost, wind and light discarding cost and pollutant emission punishment cost, and the concrete steps are as follows:
wherein F is the running cost of the micro-grid system; t is a scheduling period, and taking 24 hours; p (P) n.t Generating power for the WT, the PV and the MT, charging and discharging the BAT, trading electric quantity with a large power grid, and consuming power by P2G and CCUS; c (C) on,i Maintaining coefficients for the operation of the micro-grid equipment; c (C) gas 、Natural gas price and natural gas consumed by the micro gas turbine respectively; x is X t The time-sharing electricity price is; p (P) grid,t Trade electric quantity for micro-grid and large grid; c (C) dis The unit wind and light discarding punishment cost is given; p (P) dis,t The electric quantity is discarded for discarding wind and light.
(2) Micro grid system operating voltage bias minimization
The high-permeability access of the distributed power supply to the micro-grid is beneficial to increasing the voltage of the nodes, reducing the voltage deviation among the nodes and stabilizing the voltage level. When performing the operation planning and parallel planning of the "source-grid-load-store" micro-grid, it is necessary to make the micro-grid system operation voltage deviation as small as possible to improve the voltage quality. In engineering practice, the running voltage deviation of the micro-grid system can be represented by the average value of the actual voltage amplitude of the node and the rated value deviation:
wherein DeltaU is node average voltage deviation and represents the running voltage deviation level of the micro-grid system; n is the node number of the micro-grid system; u (U) i 、U iref Respectively the actual voltage amplitude and the rated voltage amplitude of the node i after the distributed power supply is connected, U iref Typically 1.0p.u.
(3) Micro grid system carbon emission cost minimization
For a conventional energy unit, it would incur additional carbon trade costs,
wherein F is c Carbon emission cost for the micro-grid system; t is a scheduling period, taking 24 hours; p (P) t Trading power for a micro grid and a large grid; c is the carbon emission price of the unit electric quantity of the large power grid; s is the actual carbon emission; m is free carbon emission; c is the unit carbon trade price.
It should be noted that when the running cost of the micro-grid system is minimized, the running voltage deviation of the micro-grid system is minimized, and the carbon emission cost of the micro-grid system is minimized as the objective function, each objective function needs to be subjected to per unit, for example, the maximum value in the iterative process of the running cost of the micro-grid system, the running voltage deviation of the micro-grid system, and the carbon emission cost of the micro-grid system needs to be taken as the reference value, and the per unit is that:
the per unit value is:
the reference value B is denoted by the subscript B, and the per unit value is denoted by the subscript x.
The micro-grid power balance constraint, the micro-gas turbine climbing constraint, the distributed power supply power constraint, the micro-grid and large-grid energy interaction constraint, the storage battery charge-discharge constraint and the node voltage constraint can be respectively shown as follows:
(1) Micro-grid power balance constraint
P WT +P PV +P MT +P ESS +P gird ＝P L (13)
Wherein P is WT 、P PV 、P MT 、P ESS 、P L WT, PV, MT, fuel respectivelyThe output power of the battery and the accumulator.
(2) Climbing constraint of miniature gas turbine
R i,min Δt≤P i,t -P i,t-1 ≤R i,max Δt (14)
Wherein R is i,min 、R i,max The upper and lower limits of the output active power are respectively.
(3) Distributed power supply power constraints
P i,min ≤P i ≤P i,max (15)
Wherein P is i,min 、P i,max The minimum maximum power of the ith distributed power supply, respectively.
(4) Micro-grid and large grid energy interaction constraint
P gird,min ≤P gird ≤P gird,max (16)
Wherein P is gird,min 、P gird,max The minimum and maximum interaction power between the micro grid and the large grid, respectively.
(5) Storage battery charge-discharge constraint
S min ≤S t ≤S max
S o ＝S T
X t ·Y t ＝0
0≤P cha,t ≤0.2E b,n X t
0≤P dis,t ≤0.2E b,n Y t
Wherein S is t 、S max 、S min The charge state of the storage battery and the upper limit and the lower limit of the charge state of the storage battery are respectively; s is S o 、S T The initial charge state and the final charge state of the storage battery on the same day respectively; x is X T 、Y t Respectively charge and discharge states of the storage battery, wherein X t ∈{0,1}、Y t ∈{0,1}；P cha,t 、P dis,t Respectively charging and discharging power of the storage battery at the moment t; e (E) b,n Is the capacity of the storage battery; n (N) 1 、N 2 The maximum charge and discharge times of the storage battery are obtained.
(6) Node voltage constraint
V i,min ≤V i ≤V i,max (18)
Wherein V is i,min And V i,max The minimum allowable voltage amplitude value and the maximum allowable voltage amplitude value of the node i are respectively.
In some embodiments, the micro-grid source-grid load storage collaborative scheduling model can be initially solved according to historical operation data to obtain an initial solution, then a war strategy optimization algorithm is adopted to continuously iterate and optimize the initial solution to obtain an optimal solution, namely the initial solution is used as a army in the war strategy optimization algorithm, so that the optimal solution is continuously iterated and optimized, and the optimal solution is rapidly obtained.
As one example, the war strategy optimization algorithm used herein may be as follows:
(1) Attack strategy
The soldier adjusts the position of the soldier according to the positions of the king and the commander, which is a main strategy for updating the position of the soldier, and can be specifically as follows:
X i (t+1)＝X i (t)+2·rand·(C-King)+rand·(W i ·King-X i (t)) (19)
wherein X is i (t+1) is the new position of the soldier in the t+1 iteration, X i (t) soldier position in the t-th iteration, C and King are commander and King, W i Is the weight of the King position.
(2) Soldier rank and weight update
The soldier selects better positions on the battlefield, the soldier's rank is improved along with the improvement of the battlefield, and the soldier's rank can be specifically as follows:
X i (t+1)＝(X i (t+1))·(F n ≥F p )+(X i (t))·(F n <F p )
R i ＝(R i +1)·(F n ≥F p )+(R i )·(F n <F p ) (20)
wherein F is n To fight soldiers in new locations, F p R is the fight force of soldiers on old battle lands i Is the soldier's rank.
Ranking soldiers according to soldier effectiveness, the updated weights may be as follows:
W i ＝W i ·(1-R i /T) α (21)
where T is the total number of iterations and α is an exponential factor.
Weight factor W i Closely related to the search capabilities of the algorithm. In order to improve the global searching capability in the early stage and the convergence capability in the later stage, the exponential factor is set to be dynamic, which can be specifically as follows:
α＝2(1-e -t/T ) (22)
where t is the number of iterations of the algorithm.
(3) Defending strategy
The soldier measures his distance from the king, protects the safety of the king during war, and can specifically be as follows:
X i (t+1)＝X i (t)+2·rand·(King-X rand (t))+rand·W i ·(C-X i (t)) (23)
wherein X is rand (t) is the soldier's random position in the t-th iteration.
(4) Weak soldier updating strategy (i.e. weak soldier replacement/repositioning strategy)
The weak soldier position is updated by adopting two strategies, one is to randomly generate a new soldier through a formula (24), and the other is to place the weak soldier at the middle position of the whole battlefield through a formula (25):
X w (t+1)＝Lb+rand·(Ub-Lb) (24)
X w (t+1)＝-(1-randn)·(X w (t)-median(X))+King (25)
wherein X is w (t+1) is the soldier replaced in the t+1st iteration, ub and Lb represent the upper and lower limits of the search space. random numbers with randn uniformly distributed between 0 and 1, medium (X) represents median function.
Notably, in order to improve the optimization capability of the war strategy optimization algorithm, another new weak soldier updating strategy is proposed, which specifically comprises the following steps:
randomly updating weak soldiers in the early stage of iterative optimization;
in the middle of iterative optimization, the weak soldiers are gradually moved to the middle of a battlefield while being randomly updated;
in the later stage of iterative optimization, weak soldiers are placed in the middle of a battlefield.
Wherein, iterative optimization early passRepresenting that iterative optimization metaphase passes +.>Representing the iterative optimization late pass +.>T represents the number of iterations and T represents the total number of iterations.
Illustratively, the equation (26) may be as follows:
where beta is a time-weighted search factor,
and S130, generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result.
And S140, carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy.
For example, the source network charge storage collaborative scheduling policy of the micro-grid may be sent to an intelligent scheduling center to which the micro-grid belongs, and the intelligent scheduling center performs source network charge storage collaborative scheduling on the micro-grid.
In the embodiment of the disclosure, the source-network-charge-storage multi-energy element cooperative scheduling strategy of the micro-grid can be generated based on historical operation data of the micro-grid under the cooperative action of the source-network-charge-storage multi-energy elements, and the source-network-charge-storage cooperative scheduling is performed on the micro-grid on the basis, so that the efficient consumption of renewable energy sources is promoted to the greatest extent, the carbon emission is reduced, and the electricity quality of users is ensured.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 2 illustrates a block diagram of a micro-grid source-network load-storage co-scheduling apparatus provided by an embodiment of the present disclosure, and as shown in fig. 2, a micro-grid source-network load-storage co-scheduling apparatus 200 may include:
an obtaining module 210, configured to obtain historical operation data of the micro grid;
the solving module 220 is configured to solve the micro-grid source-grid load storage collaborative scheduling model according to the historical operation data by adopting a war strategy optimization algorithm;
the generating module 230 is configured to generate a micro-grid source network load storage collaborative scheduling policy according to the solution result;
and the scheduling module 240 is configured to perform source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling policy.
In some embodiments, the solution module 220 is specifically configured to:
carrying out initial solution on the micro-grid source network load storage collaborative scheduling model according to the historical operation data to obtain an initial solution;
and adopting the war strategy optimization algorithm to continuously and iteratively optimize the initial solution to obtain an optimal solution.
It can be understood that each module/unit in the micro-grid source network load storage cooperative scheduling apparatus 200 shown in fig. 2 has a function of implementing each step in the micro-grid source network load storage cooperative scheduling method 100 shown in fig. 1, and can achieve a corresponding technical effect, which is not described herein for brevity.
Fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure. Electronic device 300 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 3, the electronic device 300 may include a computing unit 301 that may perform various suitable actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM302, and the RAM303 are connected to each other by a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Various components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program, tangibly embodied on a computer-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 300 via the ROM302 and/or the communication unit 309. One or more of the steps of the method 100 described above may be performed when the computer program is loaded into RAM303 and executed by the computing unit 301. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 by any other suitable means (e.g. by means of firmware).
The various embodiments described above herein may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-a-chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a computer-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium would include one or more wire-based electrical connections, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the present disclosure further provides a non-transitory computer readable storage medium storing computer instructions, where the computer instructions are configured to cause a computer to perform the method 100 and achieve corresponding technical effects achieved by performing the method according to the embodiments of the present disclosure, which are not described herein for brevity.
In addition, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method 100.
To provide for interaction with a user, the embodiments described above may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The above-described embodiments may be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (10)

1. The utility model provides a micro-grid source network load storage collaborative scheduling method, which is characterized by comprising the following steps:
acquiring historical operation data of a micro-grid;
solving a micro-grid source network load storage collaborative scheduling model according to the historical operation data by adopting a war strategy optimization algorithm;
generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result;
and carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy.
2. The method of claim 1, wherein the historical operating data comprises: line historical operation data, wind power historical output data, photovoltaic historical output data, micro gas turbine historical output data and storage battery historical data.
3. The method of claim 1, wherein solving the micro-grid source network load storage collaborative scheduling model according to the historical operating data using a war strategy optimization algorithm comprises:
carrying out initial solution on the micro-grid source network load storage collaborative scheduling model according to the historical operation data to obtain an initial solution;
and adopting the war strategy optimization algorithm to continuously and iteratively optimize the initial solution to obtain an optimal solution.
4. A method according to claim 3, wherein, when a war strategy optimization algorithm is employed, the initial solution is continuously iteratively optimized, the weak soldier update strategy of the war strategy optimization algorithm is:
randomly updating weak soldiers in the early stage of iterative optimization;
in the middle of iterative optimization, the weak soldiers are gradually moved to the middle of a battlefield while being randomly updated;
in the later stage of iterative optimization, the weak soldier is placed in the middle of a battlefield;
the iterative optimization early-stage passesRepresenting that said iterative optimization metaphase passes +.> Representing that the iterative optimization later phase passes +.>T represents the number of iterations and T represents the total number of iterations.
5. The method of claim 1, wherein the microgrid source network load storage co-scheduling model comprises: a micro-grid system model and a multi-objective optimization model.
6. The method of claim 5, wherein the microgrid system model comprises: photovoltaic power generation model, wind power generation model, micro gas turbine power generation model and storage battery model;
the multi-objective optimization model takes the minimum running cost of the micro-grid system, the minimum running voltage deviation of the micro-grid system and the minimum carbon emission cost of the micro-grid system as objective functions, and takes micro-grid power balance constraint, micro-gas turbine climbing constraint, distributed power supply power constraint, micro-grid and large grid energy interaction constraint, storage battery charging and discharging constraint and node voltage constraint as constraint conditions.
7. A micro-grid source-grid load storage collaborative scheduling device, characterized in that the device comprises:
the acquisition module is used for acquiring historical operation data of the micro-grid;
the solving module is used for solving the micro-grid source network load storage collaborative scheduling model according to the historical operation data by adopting a war strategy optimization algorithm;
the generation module is used for generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result;
and the scheduling module is used for carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy.
8. The apparatus of claim 7, wherein the solving module is specifically configured to:
carrying out initial solution on the micro-grid source network load storage collaborative scheduling model according to the historical operation data to obtain an initial solution;
and adopting the war strategy optimization algorithm to continuously and iteratively optimize the initial solution to obtain an optimal solution.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-6.
CN202310737372.1A 2023-06-20 2023-06-20 Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium Pending CN116885795A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310737372.1A CN116885795A (en) 2023-06-20 2023-06-20 Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310737372.1A CN116885795A (en) 2023-06-20 2023-06-20 Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium

Publications (1)

Publication Number Publication Date
CN116885795A true CN116885795A (en) 2023-10-13

Family

ID=88253865

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310737372.1A Pending CN116885795A (en) 2023-06-20 2023-06-20 Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium

Country Status (1)

Country Link
CN (1) CN116885795A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117787750A (en) * 2023-12-29 2024-03-29 烽光新能(上海)科技发展有限公司 Energy collaborative scheduling method, device, computer equipment and storage medium
CN118367555A (en) * 2024-06-20 2024-07-19 东莞理工学院 Micro-grid energy storage scheduling collaborative optimization method for big data analysis

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117787750A (en) * 2023-12-29 2024-03-29 烽光新能(上海)科技发展有限公司 Energy collaborative scheduling method, device, computer equipment and storage medium
CN118367555A (en) * 2024-06-20 2024-07-19 东莞理工学院 Micro-grid energy storage scheduling collaborative optimization method for big data analysis

Similar Documents

Publication Publication Date Title
CN116885795A (en) Micro-grid source-grid load storage collaborative scheduling method, device, equipment and storage medium
Hassan et al. Optimal reactive power dispatch with time-varying demand and renewable energy uncertainty using Rao-3 algorithm
CN110070292B (en) Micro-grid economic dispatching method based on cross variation whale optimization algorithm
CN113285490B (en) Power system scheduling method, device, computer equipment and storage medium
CN110766239A (en) Micro-grid optimization scheduling method based on firework algorithm
CN110518634A (en) Wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing
Wang et al. An improved min-max power dispatching method for integration of variable renewable energy
CN118232424B (en) Multi-objective optimized source-charge storage capacity configuration method and device
CN117154736A (en) Method and system for optimizing deep peak shaving of thermal power unit by participation of hybrid energy storage system
CN111523204B (en) Optimal configuration solving method for grid-connected comprehensive energy grid electricity-gas energy storage system
CN116957362A (en) Multi-target planning method and system for regional comprehensive energy system
Omar et al. Multi-Objective Optimization of a Stand-alone Hybrid PV/wind/battery/diesel Micro-grid
CN116599087B (en) Frequency modulation strategy optimization method and system of energy storage system
CN117543706A (en) Hybrid energy storage configuration method and system based on micro-grid wind-solar energy storage system
CN116231669A (en) Data processing method, device, electronic equipment and storage medium
Altin Artificial bee colony algorithm based very fast renewable energy system optimization tool design
Isa et al. A Review of Optimization Approaches for Optimal Sizing and Placement of Battery Energy Storage System (BESS)
CN116432478B (en) Energy determination method, device, equipment and medium for electric power system
CN117318116A (en) Source network load storage collaborative scheduling method and device applied to micro-grid group
CN117977655B (en) Offshore wind power energy storage configuration method and device, storage medium and terminal
CN115395543B (en) 5G fusion power distribution network base station shared energy storage method and system based on matching
Yang et al. Multi-source scheduling method on supply side of microgrid based on reinforcement learning algorithm
Panwar et al. Eco-economie sizing of autonomous hybrid energy system (AHES) using particle swarm optimization (PSO)
CN118446089A (en) Micro-grid source network load storage optimal configuration method based on improved artificial fish swarm algorithm
CN117220317A (en) Distributed energy storage system scheduling method, device, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination