CN106953358A - A kind of active distribution network Optimized Operation strategy determines method - Google Patents
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- 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"
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- 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
- 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]
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- H02J3/385—
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- H02J3/386—
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
Abstract
A kind of active distribution network Optimized Operation strategy determines method, using the active distribution network operating cost in a complete cycle as regulation goal function, optimizes scheduling to the active distribution network comprising " scene combustion is stored up ", step includes：（1）Variable format is controlled according to active distribution network scheduling requirement and specifically, the active distribution network regulation goal function and each constraints are set up in a complete cycle；（2）Wind/optical electric field active power output data that collection active distribution network region is included, send data to active distribution network dispatching control center, its apoplexy/optical electric field is generated electricity in MPPT maximum power point tracking mode, it is ensured that green energy resource maximum utilizationization；（3）With reference to operating cost object function in active distribution network complete cycle, alternative is found in the range of constraints using simulated annealing, until algorithmic statement, exports now prioritization scheme and target function value, complete active distribution network Optimized Operation strategy and determine work.
Description
Technical field
Method, category electric power active distribution network technology neck are determined the present invention relates to a kind of active distribution network Optimized Operation strategy
Domain.
Background technology
The problems such as increasingly serious environmental pollution and short conventional fossil fuel, drives distributed generation technology especially
Renewable energy power generation technology is developed rapidly, and following power distribution network certainly will will meet the compatibility generated electricity to distribution type renewable energy
Bag is simultaneously.Active distribution network can be achieved on distributed power source wide in power distribution network as following development trend of intelligent distribution network
General access and the important technical of hypersynchronous.Active distribution network may be defined as：Can be with Comprehensive Control distributed resource (for example
Distributed energy, flexible load, energy storage etc.) power distribution network, flexible network adjustment technology can be used to realize the effective of trend
Management, distributed energy undertakes the supporting role certain to system on the basis of its rational supervision environment and access criterion.
The big notable feature that active distribution network is different from conventional electrical distribution net shows the distributed generation unit of access, storage
Energy unit and microgrid unit etc. are all controllable for power distribution network operations staff, and distributed energy will participate in the fortune of network
Row scheduling, not simple connection in the past, this will assign active distribution network management and running abundanter content, and be not only
The adjustment of interconnection switch in conventional electrical distribution net.The Optimized Operation strategy of active distribution network is that active distribution network is real to distributed power source
Apply active management and realize the core technology and important means of network security economical operation.But intermittent renewable energy source power
The intrinsic uncertainty of output, energy-storage system is limited the coupling correlation on caused different time section by its own energy
And in power distribution network interconnection switch position flexibility and changeability so that the Optimized Operation strategy of active distribution network is sufficiently complex, base
Do not applied in the Optimal Operation Model and its computational methods of traditional optimal load flow for active distribution network.
In face of active distribution network complicated and changeable, to ensure its safe and stable operation, active distribution network Optimized Operation is carried out
Strategy study is of great importance.
The content of the invention
The problem to be solved in the present invention is to propose that a kind of active distribution network Optimized Operation strategy determines method, passes through simulation
Annealing algorithm optimizes control to scattered energy-storage system and other distributed generation units and interconnection switch, with active distribution network
The minimum target of operating cost within the whole service cycle, faster, more accurately determines the Optimized Operation in active distribution network
Strategy.
Realize that technical scheme is as follows：A kind of active distribution network Optimized Operation strategy determines method, methods described
Using the active distribution network operating cost in a complete cycle as regulation goal function, with reference to simulated annealing to comprising " wind-
The active distribution network of light-combustion-storage " optimizes scheduling, and step is as follows：
Step 1：Variable format is controlled according to active distribution network scheduling requirement and specifically, institute is set up in a complete cycle
State active distribution network regulation goal function and each constraints；
Step 2：Wind/optical electric field active power output data that collection active distribution network region is included, send data to
Active distribution network dispatching control center, its apoplexy/optical electric field is generated electricity in MPPT maximum power point tracking mode, it is ensured that green energy
Source maximum utilizationization；
Step 3：With reference to operating cost object function in active distribution network complete cycle, using simulated annealing in constraint
Alternative is found in condition and range, until algorithmic statement, exports now prioritization scheme and target function value, active distribution is completed
Network optimization scheduling strategy determines work.
The active distribution network Optimized Operation strategy carries out Load flow calculation from forward-backward sweep method to the active distribution network
To determine active distribution network Optimized Operation strategy, the active distribution network includes controllable energy source unit, energy-storage units, generator machine
Terminal voltage, reactive power compensator and transformer tapping respectively control variable.
Regulation goal function of the active distribution network in a complete cycle is shown below：
In formula, k is the unit number of stages that can be divided in the full schedule cycle, thinks that each distribution is sent out for each stage
Electric unit is exerted oneself, energy-storage units are exerted oneself and payload is constant；Δ T is the duration in unit stage；L calculates for whole optimal load flow
The feeder line number for the supplying power allocation regional internet being related to；CgAnd P (t)g(t) be respectively the g articles feeder line t electricity price cost and
The g articles feeder line exports active power；N is the number of distributed generation unit, and distributed generation unit here refers to that power can
The generator unit of regulation；CiAnd P (t)DG-i(t) it is respectively then i-th of distributed generation unit in the cost of electricity-generating of t and has
Work(is exerted oneself.
The constraints of the active distribution network Optimized Operation strategy, constraints is as follows：
In formula, PESS-j(t) it is charge-discharge electric power of j-th of energy-storage units in t；O (t) is the interconnection switch of t
Location schemes；WithThe lower limit of the power and the upper limit of respectively i-th distributed generation unit；WithPoint
Not Wei j-th of energy-storage units power is offline and the upper limit；V (O (t)) is the location schemes of power distribution network interconnection switch；G is distribution
The radial structure of net；Ej(t) it is dump energy of j-th of energy-storage units in t；WithRespectively j-th storage
Minimum value and maximum that energy unit dump energy is allowed.
Regulation goal function of the active distribution network in a complete cycle, is related to content including different feeder lines not
Electricity price cost in the same time, the active power of different feeder line outlets, the number and difference of power adjustable distributed generation unit
Power adjustable distributed generation unit is in factors such as cost of electricity-generatings and active power output not in the same time.
The constraints of the active distribution network Optimized Operation strategy includes charge and discharge electric work energy-storage units at a time
Rate, power distribution network interconnection switch location schemes, the lower limit of the power of distributed generation unit and the upper limit at a certain moment, different energy storage lists
The lower limit of the power and the upper limit of member, different energy-storage units dump energy at a time, different energy-storage units dump energies permit
Perhaps minimum value and maximum.
The generator unit of the power adjustable section, including fuel cell and diesel-driven generator.
Beneficial effects of the present invention mainly have：The present invention is when carrying out active distribution network Optimized Operation strategy study, with master
The minimum target of dynamic operating cost of the power distribution network within the whole service cycle, by sending out scattered energy-storage system and other distributions
The optimal control of electric unit and interconnection switch, faster, is more accurately determined excellent in active distribution network using simulated annealing
Change scheduling strategy, realize the minimum control of operating cost, it is ensured that active distribution network economic benefit is optimal.
Brief description of the drawings
Fig. 1 is implementation steps FB(flow block) of the present invention.
Embodiment
In order to illustrate the objects, technical solutions and advantages of the present invention, below in conjunction with drawings and Examples, to present invention progress
It is further to describe in detail.
In the examples below, Fig. 1 determines method implementation steps for active distribution network Optimized Operation strategy.
The present embodiment active distribution network Optimized Operation strategy carries out Load flow calculation, the active distribution from forward-backward sweep method
Network optimization scheduling strategy determined in method, active distribution network include controllable energy source unit, energy-storage units, generator terminal voltage,
Reactive power compensator and transformer tapping etc. respectively control variable, and the active distribution network Optimized Operation strategy determines that method implements step
It is rapid as follows：
(1) according to active distribution network feature and objectives requirement, active distribution network tune is set up in a complete cycle
Object function is spent, regulation goal function of the active distribution network in a complete cycle is shown below：
In formula, k is the unit number of stages that can be divided in the full schedule cycle, thinks that each distribution is sent out for each stage
Electric unit is exerted oneself, energy-storage units are exerted oneself and payload is constant；Δ T is the duration in unit stage；L calculates for whole optimal load flow
The feeder line number for the supplying power allocation regional internet being related to；CgAnd P (t)g(t) be respectively the g articles feeder line t electricity price cost and
The g articles feeder line exports active power；N is the number of distributed generation unit, and distributed generation unit here refers to that power can
The generator unit of regulation, such as fuel cell and diesel-driven generator；CiAnd P (t)DG-i(t) it is respectively then distributed i-th
Cost of electricity-generating and active power output of the generator unit in t；
(2) required to determine the constraint of active distribution network Optimized Operation strategy according to control types of variables and probabilistic load flow
Condition, constraints is as follows：
In formula, PESS-j(t) it is charge-discharge electric power of j-th of energy-storage units in t；O (t) is the interconnection switch of t
Location schemes；WithThe lower limit of the power and the upper limit of respectively i-th distributed generation unit；WithPoint
Not Wei j-th of energy-storage units power is offline and the upper limit；V (O (t)) is the location schemes of power distribution network interconnection switch；G is distribution
The radial structure of net；Ej(t) it is dump energy of j-th of energy-storage units in t；WithRespectively j-th storage
Minimum value and maximum that energy unit dump energy is allowed；
(3) according to the control types of variables of active distribution network, optional one group of original state x in constraints0, calculate master
The Optimized Operation target function value f (x of dynamic power distribution network0), and select initial control temperature T0With markovian length, it is described
Markov chain length chooses mode：tk+1=α × tk, k=0,1,2 ..., wherein α is one close to 1 constant, the decay
The attenuation of function pair control parameter is successively decreased with algorithm process；The tolerance is 1 × 10-8, the attenuation parameter is 0.95；
(4) random perturbation is produced in solution space, producing function with state produces a new state x1, calculate
Its Optimized Operation target function value f (x1), the state produces function, if assuming | Ωt| it is constant, then expression formula is as follows：
(5) function is received according to state and judges whether receiving：If f (x1)<f(x0), then receive new state x1For current shape
State, otherwise judges whether to receive x according to Metropolis criterions1, make current state be equal to x if receiving1, made if not receiving
Current state is equal to x0, the state receives function, if setting L, (S is f) example in optimization, S represents solution space, f:S
→ R represents solution space to the mapping of real number, and t is the control parameter of temperature during simulated annealing, it is assumed that (S f) exists L
Neighborhood and the generation mechanism accordingly solved, f (i), f (j) are respectively to correspond to solution i, j target function value, then Metropolis
Criterion can be expressed as：
(6) according to convergence criterion, whether judgement sampling process terminates, and carries out next step if terminating, otherwise carries out step
(4)；
(7) according to temperature cooling scheme reduction control temperature T；
(8) according to convergence criterion, judge whether annealing process terminates, carry out next step if terminating, otherwise carry out step
(4), the convergence precision is 1 × 10-8；
(9) output current feedback control scheme and target function value complete active distribution as optimum control scheme and optimal solution
Network optimization scheduling strategy is determined；
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope of this specification record is all considered to be.
Claims (8)
1. a kind of active distribution network Optimized Operation strategy determines method, it is characterised in that methods described is with a complete cycle
Active distribution network operating cost be regulation goal function, with reference to simulated annealing to including the active of " wind-light-combustion-storage "
Power distribution network optimizes scheduling, and step is as follows：
Step 1：Variable format is controlled according to active distribution network scheduling requirement and specifically, the master is set up in a complete cycle
Dynamic power distribution network regulation goal function and each constraints；
Step 2：Wind/optical electric field active power output data that collection active distribution network region is included, send data to actively
Power distribution network dispatching control center, its apoplexy/optical electric field is generated electricity in MPPT maximum power point tracking mode, it is ensured that green energy resource is most
Big utilizationization；
Step 3：With reference to operating cost object function in active distribution network complete cycle, using simulated annealing in constraints
In the range of find alternative, until algorithmic statement, output now prioritization scheme and target function value complete active distribution network excellent
Change scheduling strategy and determine work.
2. a kind of active distribution network Optimized Operation strategy according to claim 1 determines method, it is characterised in that the master
Dynamic power distribution network Optimized Operation strategy carries out Load flow calculation to determine active distribution from forward-backward sweep method to the active distribution network
Network optimization scheduling strategy, the active distribution network includes controllable energy source unit, energy-storage units, generator terminal voltage, idle benefit
Repay device and transformer tapping respectively controls variable.
3. a kind of active distribution network Optimized Operation strategy according to claim 1 determines method, it is characterised in that the master
Dynamic regulation goal function of the power distribution network in a complete cycle is shown below：
In formula, k is the unit number of stages that can be divided in the full schedule cycle, and each distributed power generation list is thought for each stage
Member is exerted oneself, energy-storage units are exerted oneself and payload is constant；△ T are the duration in unit stage；L is that the calculating of whole optimal load flow is related to
Supplying power allocation regional internet feeder line number；CgAnd P (t)g(t) it is respectively the g articles feeder line in the electricity price cost of t and the g articles
Feeder line exports active power；N is the number of distributed generation unit, and distributed generation unit here refers to power adjustable section
Generator unit；CiAnd P (t)DG-i(t) it is respectively then cost of electricity-generating and active power output of i-th of distributed generation unit in t.
4. a kind of active distribution network Optimized Operation strategy according to claim 1 determines method, it is characterised in that the master
The constraints of dynamic power distribution network Optimized Operation strategy, constraints is as follows：
In formula, PESS-j(t) it is charge-discharge electric power of j-th of energy-storage units in t；O (t) is the interconnection switch position of t
Scheme；WithThe lower limit of the power and the upper limit of respectively i-th distributed generation unit；WithRespectively
The power of j-th of energy-storage units is offline and the upper limit；V (O (t)) is the location schemes of power distribution network interconnection switch；G is power distribution network
Radial structure；Ej(t) it is dump energy of j-th of energy-storage units in t；WithRespectively j-th energy storage list
Minimum value and maximum that first dump energy is allowed.
5. a kind of active distribution network Optimized Operation strategy according to claim 3 determines method, it is characterised in that the master
Dynamic regulation goal function of the power distribution network in a complete cycle, be related to content including different feeder lines electricity price not in the same time into
Originally, active power, the number of power adjustable distributed generation unit and the adjustable distribution of different capacity of different feeder line outlets
Generator unit is in cost of electricity-generating not in the same time and active power output factor.
6. a kind of active distribution network Optimized Operation strategy according to claim 4 determines method, it is characterised in that the master
The constraints of dynamic power distribution network Optimized Operation strategy includes energy-storage units charge-discharge electric powers at a time, a certain moment and matched somebody with somebody
Power network interconnection switch location schemes, the lower limit of the power of distributed generation unit and the upper limit, the lower limit of the power of different energy-storage units and
Minimum value that the upper limit, different energy-storage units dump energy at a time, different energy-storage units dump energies are allowed and most
Big value.
7. a kind of active distribution network Optimized Operation strategy according to claim 3 determines method, it is characterised in that the work(
The adjustable generator unit of rate, including fuel cell and diesel-driven generator.
8. a kind of active distribution network Optimized Operation strategy according to claim 1 determines method, it is characterised in that it is described
Alternative is found in the range of constraints, step is as follows：
(1) according to the control types of variables of active distribution network, optional one group of original state x in constraints0, calculate and actively match somebody with somebody
The Optimized Operation target function value f (x of power network0), and select initial control temperature T0With markovian length, the Ma Er
Section's husband's chain length chooses mode：tk+1=α × tk, k=0,1,2 ..., wherein α is one close to 1 constant, the attenuation function
Attenuation to control parameter is successively decreased with algorithm process；The tolerance is 1 × 10-8, the attenuation parameter is 0.95；
(2) random perturbation is produced in solution space, producing function with state produces a new state x1, calculate its excellent
Change regulation goal functional value f (x1), the state produces function, if assuming | Ωt| it is constant, then expression formula is as follows：
(3) function is received according to state and judges whether receiving：If f (x1)<f(x0), then receive new state x1For current state,
Otherwise judge whether to receive x according to Metropolis criterions1, make current state be equal to x if receiving1, make current if not receiving
State is equal to x0, the state receives function, if setting L, (S is f) example in optimization, S represents solution space, f:S → R tables
Show solution space to the mapping of real number, t is the control parameter of temperature during simulated annealing, it is assumed that L (S, f) exist neighborhood with
And the generation mechanism accordingly solved, f (i), f (j) they are respectively to correspond to solution i, j target function value, then Metropolis criterions can
It is expressed as：
(4) according to convergence criterion, whether judgement sampling process is terminated, and next step is carried out if terminating, and otherwise carries out step (2)；
(5) according to temperature cooling scheme reduction control temperature T；
(6) according to convergence criterion, judge whether annealing process terminates, next step carried out if terminating, otherwise carry out step (2),
The convergence precision is 1 × 10-8；
(7) output current feedback control scheme and target function value complete active distribution network excellent as optimum control scheme and optimal solution
Change scheduling strategy to determine.
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Cited By (5)
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| CN108832862A (en) * | 2018-06-14 | 2018-11-16 | 苏州工业园区职业技术学院 | A kind of electric machine controller and its control method for a variety of variable-frequency washing machines |
| CN108844190A (en) * | 2018-06-20 | 2018-11-20 | 中国科学院自动化研究所 | Air-conditioning self study optimal control system |
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| WO2020082409A1 (en) * | 2018-10-22 | 2020-04-30 | 清华大学 | Non-iterative decomposition-coordination dynamic scheduling method for power transmission and distribution networks |
| CN111563831A (en) * | 2020-05-18 | 2020-08-21 | 南京航空航天大学 | Source network load storage cooperative scheduling method for ubiquitous power Internet of things |
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| 杨珺等: "基于模拟退火遗传算法的分散式风电选址定容", 《东北电力技术》 * |
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| CN108844190A (en) * | 2018-06-20 | 2018-11-20 | 中国科学院自动化研究所 | Air-conditioning self study optimal control system |
| CN108844190B (en) * | 2018-06-20 | 2020-02-28 | 中国科学院自动化研究所 | Air conditioner self-learning optimal control system |
| WO2020082409A1 (en) * | 2018-10-22 | 2020-04-30 | 清华大学 | Non-iterative decomposition-coordination dynamic scheduling method for power transmission and distribution networks |
| CN110351679A (en) * | 2019-04-22 | 2019-10-18 | 鲁东大学 | A kind of wireless sensor network resource allocation methods based on improvement simulated annealing |
| CN110351679B (en) * | 2019-04-22 | 2022-03-18 | 鲁东大学 | Wireless sensor network resource allocation method based on improved simulated annealing |
| CN111563831A (en) * | 2020-05-18 | 2020-08-21 | 南京航空航天大学 | Source network load storage cooperative scheduling method for ubiquitous power Internet of things |
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