CN109494813A - A kind of power dispatching method, electronic equipment and storage medium - Google Patents
A kind of power dispatching method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN109494813A CN109494813A CN201811642329.2A CN201811642329A CN109494813A CN 109494813 A CN109494813 A CN 109494813A CN 201811642329 A CN201811642329 A CN 201811642329A CN 109494813 A CN109494813 A CN 109494813A
- Authority
- CN
- China
- Prior art keywords
- power
- micro
- capacitance sensor
- cost
- population
- 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
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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention provides a kind of power dispatching method, including step constructs objective function, generates constraint condition, solves approximate optimal solution.The invention further relates to storage mediums, electronic equipment；The present invention is fast using speed of searching optimization, the strong particle swarm algorithm of search capability optimizes scheduling to the micro-capacitance sensor of rural remote areas, and two populations are generated by optimizing algorithm, it solves particle in single specie to rapidly converge near population history optimal value, and when population history optimal value is local optimum, single specie converges on local optimum, the problem of overall optimal solution of day operation cost minimization can not be approached, realize the micro-capacitance sensor scheduling strategy for acquiring the near-optimization in a period of time in global scope to the micro-capacitance sensor of rural remote areas, the scheduling strategy can approach the overall optimal solution of day operation cost minimization, scheme can be provided to each time point, avoid the unreasonable result generated using simple strategy, benefit is greater than simple charge and discharge scheduling strategy.
Description
Technical field
The invention belongs to remote countryside area micro-capacitance sensor economic optimization technical fields, are a kind of electricity based on particle swarm algorithm
Power dispatching method, electronic equipment, storage medium.
Background technique
China, area region, remote countryside is wide, with a varied topography, brings many problems to power grid power transmission and distribution.Too long is defeated
Electric line leads to a large amount of line loss, and the cost of line upkeep also largely increases, and micro-capacitance sensor is one kind by load and micro battery
And the organic system that energy storage device collectively constitutes, be the important component of smart grid, can be realized self-protection, management and
Control, can both be incorporated into the power networks with external electrical network, can also be with isolated operation.Remote countryside area can exist according to its actual demand
The electricity consumption that micro-capacitance sensor ensures rural households is established close to the position of user side, but because the region in remote countryside area is wide, therefore partially
The operation of remote rural area micro-capacitance sensor must primarily take into account economy, it is therefore desirable to be realized by the Economic Scheduling Policy of micro-capacitance sensor
Global optimum's scheduling scheme that quick optimizing, search operating cost minimize.But currently, the economic load dispatching to micro-capacitance sensor is mainly adopted
It is simple strategy, such as energy storage preference strategy and peak load shifting strategy.Energy storage preference strategy is to utilize energy storage device by the sun
The energy storage that energy or wind power generation peak period generate gets up, and releases in low peak period and gives electricity consumption end, to reach reduction to outside
The demand of power grid, but usually can be unable to satisfy and quickly seek due to power generation, electricity consumption unbalanced and overcharge or undercharge
The demand for global optimum's scheduling scheme that excellent, search operating cost minimizes.Peak load shifting strategy is preferentially filled up in paddy electricity valence
Generation deficiency preferentially uses self power generation when electricity price at peak, to increase the overall efficiency of micro-capacitance sensor, but is equally also easy to produce
The problem for measuring energy storage or energy storage deficiency is not achieved quick optimizing, searches for global optimum's scheduling scheme that operating cost minimizes
Effect.
Summary of the invention
For overcome the deficiencies in the prior art, a kind of power dispatching method proposed by the present invention, solves existing micro-capacitance sensor
Scheduling strategy cannot achieve to the global optimum that remote countryside area micro-capacitance sensor carries out quick optimizing, search operating cost minimizes
The problem of scheduling scheme.
The present invention provides a kind of power dispatching method, comprising the following steps:
S0, building objective function, are rung by the cost of electricity-generating of controlled distribution formula power supply, the charge and discharge cost of energy storage, demand
Answer the scheduling cost of load, the interaction cost building objective function of micro-capacitance sensor and power distribution network；
S1, constraint condition is generated, generates the output power of the corresponding controlled distribution formula power supply of the objective function about
Beam, the charge power constraint of the energy storage, discharge power constraint, scheduling whole story capacity-constrained, residual capacity constraint, the demand
Respond total power demand of load, micro-capacitance sensor constrains the actual schedule power of demand response load, auxiliary variable, micro-capacitance sensor with
Interaction power-balance constraint, micro-capacitance sensor between power distribution network buy power constraint to power distribution network, sell power constraint；
S2, solve approximate optimal solution, by PSO Algorithm under the constraint condition objective function and variable pair
The approximate optimal solution answered, the variable include the output power of controlled distribution formula power supply, energy storage inverter exchange side input fill
Electrical power and the discharge power of output, auxiliary variable, micro-capacitance sensor are filled to the power distribution network power bought and the power of sale, energy storage
Discharge condition, micro-capacitance sensor are to the actual schedule power of demand response load, micro-capacitance sensor to the purchase sale of electricity state of power distribution network, micro-capacitance sensor
Interior conventional load power and photovoltaic output power.
Further, in step so), the objective function specifically:
Wherein, t is period, NTFor dispatching cycle, CGIt (t) is the cost of electricity-generating of controlled distribution formula power supply, CSIt (t) is energy storage
Charge and discharge cost, CDRIt (t) is the scheduling cost of demand response load, CMIt (t) is the interaction cost of micro-capacitance sensor and power distribution network.
Further, in step so), the cost of electricity-generating of the controlled distribution formula power supply, the energy storage charge and discharge at
The interaction cost of sheet, the scheduling cost of the demand response load, the micro-capacitance sensor and power distribution network is successively specifically:
CG(t)=[aPG(t)+b]Δt
Wherein, CGIt (t) is cost of electricity-generating of the controlled distribution formula power supply in the t period, a, b are cost coefficient, PGIt (t) is the t period
The output power of controlled distribution formula power supply, Δ t are scheduling step-length；
Wherein, CSIt (t) is the average charge and discharge cost of energy storage t period within the investment payback time, KSIt is filled for the unit after conversion
Electric discharge cost,WithThe respectively electric discharge of the charge power of t period energy storage inverter exchange side input and output
Power, η are the efficiency for charge-discharge of energy-storage units；
CDR(t)=KDR[PDR1(t)+PDR2(t)]Δt
Wherein, CDR(t) the scheduling cost for the demand response load paid needed for the t period for micro-capacitance sensor, KDRIt is rung for demand
The unit of load is answered to dispatch cost, PDR1(t)、PDR2It (t) is auxiliary variable；
Wherein, CM(t) in the interaction cost of t period micro-capacitance sensor and power distribution network, λ (t) is the day-ahead power market electricity of power distribution network
Valence,Respectively power of the t period micro-capacitance sensor to the power distribution network power bought and sale.
Further, in step sl, the output power constraint of the controlled distribution formula power supply, controlled distribution formula electricity
The output power constraint in source, the charge power constraint of the energy storage, discharge power constraint, scheduling whole story capacity-constrained, residue are held
Measure constraint, total power demand of the demand response load, micro-capacitance sensor become the actual schedule power of demand response load, auxiliary
Interaction power-balance constraint, micro-capacitance sensor between amount constraint, micro-capacitance sensor and power distribution network buy power constraint to power distribution network, sell function
Rate constrains successively specifically:
Wherein,The peak power output of the respectively described controlled distribution formula power supply and minimum output work
Rate；
Wherein,For the maximum charge-discharge electric power that energy storage allows, USIt (t) is the charging and discharging state of energy storage, ESIt (0) is storage
Can scheduling initial time capacity,Respectively the greatest residual capacity that allows in scheduling process of energy storage and
Least residue capacity；
Wherein, PDRIt (t) is actual schedule power of the t moment micro-capacitance sensor to demand response load, DDRFor demand response load
Total power demand within dispatching cycle,Respectively maximum electricity consumption of the demand response load in t moment
Demand and minimum power demand；
PDR1(t)≥0,PDR2(t)≥0
Wherein,For the expectation electric power of t moment demand response load；
Wherein, PL(t)、PPV(t) it is respectively conventional load power and photovoltaic output power in t moment micro-capacitance sensor；
Wherein,The respectively maximum value of micro-capacitance sensor and power distribution network exchange power, UMIt (t) is micro-capacitance sensor to power distribution network
Purchase sale of electricity state.
Further, in step s 2, two populations, the optimization formula of the first population are generated by particle swarm algorithm are as follows:
The optimization formula of second population are as follows:
Wherein, k is the number of iterations, and w is inertial factor, and rand () is the random number between 0 to 1, c1、c2For study because
Son,For the current location of particle i d dimension in kth time iteration, pbest is each particle after the second population kth time iteration
History optimal value average value, gbest be two populations group's optimal value compare under optimal value.
Further, in step s 2 specifically includes the following steps:
S21, initialization population determine the size of the first population and the second population, and determine initial power:
Wherein, CiFor the initial day operation cost of i-th of particle,Respectively CiUpper and lower bound, βi
The random number for being position between 0 to 1；
Particle is considered as loose PQ node by S22, Load flow calculation, carries out Load flow calculation to micro-capacitance sensor；
S23, fitness function value is calculated, calculates each particle history optimal value of the first population, and calculate the first population
Group's optimal value, calculate the second population each particle history optimal value, and calculate the second population group's optimal value；
The particle of first population is substituted into the optimization formula of first population, obtained excellent by S24, calculation optimization new particle
New particle after change calculates the mean value of each particle history optimal value in the second population as pbest, takes first population
Group's optimal value and group's optimal value of second population substitute into second population as gbest, and by corresponding particle
Optimization formula, each particle of each population carries out range constraint by the new particle after being optimized；
S25, result judgement, judge whether to meet termination condition, are, terminate iteration, otherwise go to step S22.
A kind of electronic equipment, comprising: processor；
Memory；And program, wherein described program is stored in the memory, and is configured to by processor
It executes, described program includes for executing a kind of power dispatching method.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
A kind of power dispatching method of row.
Compared with prior art, the beneficial effects of the present invention are:
The present invention provides a kind of power dispatching method, including step constructs objective function, generates constraint condition, solves approximate
Optimal solution.The invention further relates to storage mediums, electronic equipment；The present invention is using the population that speed of searching optimization is fast, search capability is strong
Algorithm optimizes scheduling to the micro-capacitance sensor of rural remote areas, and generates two populations by optimizing algorithm, solves single
Particle rapidly converges near population history optimal value in group, and when population history optimal value is local optimum, single
The problem of group converges on local optimum, can not approach the overall optimal solution of day operation cost minimization, that is, realize to agriculture
The micro-capacitance sensor of village remote districts acquires the micro-capacitance sensor scheduling strategy of the near-optimization in a period of time, the scheduling in global scope
Strategy can approach the overall optimal solution of day operation cost minimization, and can provide scheme to each time point, avoid
It is generated using simple strategy unreasonable as a result, benefit is greater than simple charge and discharge scheduling strategy.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And can be implemented in accordance with the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention and the accompanying drawings.
A specific embodiment of the invention is shown in detail by following embodiment and its attached drawing.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of power dispatching method flow diagram of the invention；
Fig. 2 is solution approximate optimal solution steps flow chart schematic diagram of the invention.
Specific embodiment
In the following, being described further in conjunction with attached drawing and specific embodiment to the present invention, it should be noted that not
Under the premise of conflicting, new implementation can be formed between various embodiments described below or between each technical characteristic in any combination
Example.
A kind of power dispatching method, carried out in global scope for the micro-capacitance sensor to rural remote areas quick optimizing,
The overall optimal solution of day operation cost minimization is searched for, as shown in Figure 1, comprising the following steps:
S0, building objective function, are rung by the cost of electricity-generating of controlled distribution formula power supply, the charge and discharge cost of energy storage, demand
Answer the scheduling cost of load, the interaction cost building objective function of micro-capacitance sensor and power distribution network；Preferably, in step so), controllably
The cost of electricity-generating of distributed generation resource, the charge and discharge cost of energy storage, the scheduling cost of demand response load, micro-capacitance sensor and power distribution network
Interaction cost is successively specifically:
CG(t)=[aPG(t)+b]Δt
Wherein, CGIt (t) is cost of electricity-generating of the controlled distribution formula power supply in the t period, a, b are cost coefficient, PGIt (t) is the t period
The output power of controlled distribution formula power supply, Δ t are scheduling step-length, and value is 1 hour；In the present embodiment, controlled distribution formula power supply
Including miniature gas turbine, fuel cell, photovoltaic cell etc..
The operating cost of energy storage mainly considers its cost of investment and O&M cost, wherein CS(t) it is being invested for energy storage
The average charge and discharge cost of t period, K in payoff periodSFor conversion after unit charge and discharge cost,WithRespectively
The charge power of side input and the discharge power of output are exchanged for t period energy storage inverter, η is that the charge and discharge of energy-storage units are imitated
Rate.
CDR(t)=KDR[PDR1(t)+PDR2(t)]Δt
The change gesture of electricity consumption plan influences the comfort level of user, therefore micro-capacitance sensor needs to give compensation appropriate, wherein CDR
(t) the scheduling cost for the demand response load paid needed for the t period for micro-capacitance sensor, KDRIt is dispatched for the unit of demand response load
Cost, PDR1(t)、PDR2It (t) is auxiliary variable, by introducing auxiliary variable for CDR(t) it is shown as line form；
Wherein, CM(t) in the interaction cost of t period micro-capacitance sensor and power distribution network, λ (t) is the day-ahead power market electricity of power distribution network
Valence,Respectively power of the t period micro-capacitance sensor to the power distribution network power bought and sale.Preferably, exist
In step S0, objective function specifically:
I.e. the operational objective of micro-capacitance sensor is day operation cost minimization, wherein t is period, NTFor dispatching cycle, value is
24 hours, CGIt (t) is the cost of electricity-generating of controlled distribution formula power supply, CSIt (t) is the charge and discharge cost of energy storage, CDR(t) it is rung for demand
Answer the scheduling cost of load, CMIt (t) is the interaction cost of micro-capacitance sensor and power distribution network.
S1, constraint condition is generated, generates the output power constraint of the corresponding controlled distribution formula power supply of objective function, energy storage
Charge power constraint, discharge power constraint, scheduling whole story capacity-constrained, residual capacity constraint, total electricity consumption of demand response load
Demand, micro-capacitance sensor are to the interaction between the actual schedule power of demand response load, auxiliary variable constraint, micro-capacitance sensor and power distribution network
Power-balance constraint, micro-capacitance sensor buy power constraint to power distribution network, sell power constraint；Preferably, in step sl, controllable point
The output power constraint of cloth power supply, the output power constraint of controlled distribution formula power supply, the charge power constraint of energy storage, electric discharge function
Rate constraint, scheduling whole story capacity-constrained, residual capacity constraint, total power demand of demand response load, micro-capacitance sensor ring demand
The actual schedule power of load, auxiliary variable is answered to constrain, the interaction power-balance constraint between micro-capacitance sensor and power distribution network, micro-capacitance sensor
To power distribution network purchase power constraint, sell power constraint successively specifically:
Wherein,The respectively peak power output and minimum output power of controlled distribution formula power supply, point
It is not limited by its rated power and minimum load rate；Because the power response speed of controlled distribution formula power supply is adjusted relative to hour grade
It is very fast for degree, therefore do not consider that its climbing rate constrains, only consider output power constraint.
Wherein,For the maximum charge-discharge electric power that energy storage allows, mainly limited by the capacity of energy storage gird-connected inverter device
System, US(t) it is the charging and discharging state of energy storage, electric discharge is indicated when value is 1, expression charging, E when value is 0S(0) it is being adjusted for energy storage
The capacity of initial time is spent,The respectively greatest residual capacity that allows in scheduling process of energy storage and minimum is surplus
Covolume amount；Residual capacity constraint ensure that energy storage is equal in the whole story moment capacity of scheduling, be conducive to the round-robin scheduling of energy storage；Storage
Can the residual capacity constraint of day part prevent energy storage from overcharging or over-discharge, extend the service life of energy storage.
Wherein, PDRIt (t) is actual schedule power of the t moment micro-capacitance sensor to demand response load, DDRFor demand response load
Total power demand within dispatching cycle,Respectively maximum electricity consumption of the demand response load in t moment
Demand and minimum power demand, the requirement with user to comfort level are related；
PDR1(t)≥0,PDR2(t)≥0
Wherein,For the expectation electric power of t moment demand response load；
Wherein, PL(t)、PPV(t) it is respectively conventional load power and photovoltaic output power in t moment micro-capacitance sensor；
Wherein,Respectively the maximum value of micro-capacitance sensor and power distribution network exchange power, value need to consider power distribution network and micro-
The factors such as the capacity of power grid junction transformer and detailed policy, UMIt (t) is purchase sale of electricity state of the micro-capacitance sensor to power distribution network, value
Indicate that micro-capacitance sensor indicates micro-capacitance sensor to power distribution network sale of electricity to power distribution network power purchase, when value is 0 when being 1.
S2, approximate optimal solution is solved, objective function and variable are corresponding under constraint condition by PSO Algorithm
Approximate optimal solution, variable include the output power of controlled distribution formula power supply, energy storage inverter exchange side input charge power and
The discharge power of output, auxiliary variable, micro-capacitance sensor to the power distribution network power bought and the power of sale, energy storage charge and discharge shape
State, micro-capacitance sensor are normal into the purchase sale of electricity state of power distribution network, micro-capacitance sensor to actual schedule power, the micro-capacitance sensor of demand response load
Advise load power and photovoltaic output power.Because particle rapidly converges near population history optimal value in single specie, work as population
When history optimal value is local optimum, single specie converges on local optimum, can not approach the complete of day operation cost minimization
Office's optimal case, it is preferable that in step s 2, two populations, the optimization formula of the first population are generated by particle swarm algorithm are as follows:
The optimization formula of second population are as follows:
Wherein, k is the number of iterations, and w is inertial factor, and rand () is the random number between 0 to 1, c1、c2For study because
Son,For the current location of particle i d dimension in kth time iteration, pbest is each particle after the second population kth time iteration
History optimal value average value, gbest be two populations group's optimal value compare under optimal value.By the first population and
Second population avoids single specie from converging on local optimum, can approach the overall optimal solution of day operation cost minimization.
As shown in Figure 2, it is preferable that in step s 2 specifically includes the following steps:
S21, initialization population determine the size of the first population and the second population, and determine initial power:
Wherein, CiFor the initial day operation cost of i-th of particle,Respectively CiUpper and lower bound, βi
The random number for being position between 0 to 1；
Particle is considered as loose PQ node by S22, Load flow calculation, carries out Load flow calculation to micro-capacitance sensor；Specifically calculating process is
The prior art, this will not be repeated here.
S23, fitness function value is calculated, calculates each particle history optimal value of the first population, and calculate the first population
Group's optimal value, calculate the second population each particle history optimal value, and calculate the second population group's optimal value；
The particle of first population is substituted into the optimization formula of the first population, after obtaining optimization by S24, calculation optimization new particle
New particle, calculate the second population in each particle history optimal value mean value as pbest, take the group of the first population optimal
Value and group's optimal value of the second population obtain excellent as gbest, and by the optimization formula of corresponding particle the second population of substitution
The each particle of each population is carried out range constraint by the new particle after change；
S25, result judgement, judge whether to meet termination condition, are, terminate iteration, otherwise go to step S22.
A kind of electronic equipment, comprising: processor；
Memory；And program, wherein program is stored in memory, and is configured to be executed by processor, journey
Sequence includes for executing a kind of power dispatching method.
A kind of computer readable storage medium, is stored thereon with computer program, and computer program is executed by processor one
Kind power dispatching method.
The present invention provides a kind of power dispatching method, including step constructs objective function, generates constraint condition, solves approximate
Optimal solution.The invention further relates to storage mediums, electronic equipment；The present invention is using the population that speed of searching optimization is fast, search capability is strong
Algorithm optimizes scheduling to the micro-capacitance sensor of rural remote areas, and generates two populations by optimizing algorithm, solves single
Particle rapidly converges near population history optimal value in group, and when population history optimal value is local optimum, single
The problem of group converges on local optimum, can not approach the overall optimal solution of day operation cost minimization, that is, realize to agriculture
The micro-capacitance sensor of village remote districts acquires the micro-capacitance sensor scheduling strategy of the near-optimization in a period of time, the scheduling in global scope
Strategy can approach the overall optimal solution of day operation cost minimization, and can provide scheme to each time point, avoid
It is generated using simple strategy unreasonable as a result, benefit is greater than simple charge and discharge scheduling strategy.
More than, only presently preferred embodiments of the present invention is not intended to limit the present invention in any form；All current rows
The those of ordinary skill of industry can be shown in by specification attached drawing and above and swimmingly implement the present invention；But all to be familiar with sheet special
The technical staff of industry without departing from the scope of the present invention, is made a little using disclosed above technology contents
The equivalent variations of variation, modification and evolution is equivalent embodiment of the invention；Meanwhile all substantial technologicals according to the present invention
The variation, modification and evolution etc. of any equivalent variations to the above embodiments, still fall within technical solution of the present invention
Within protection scope.
Claims (8)
1. a kind of power dispatching method, which comprises the following steps:
S0, building objective function, it is negative by the cost of electricity-generating of controlled distribution formula power supply, the charge and discharge cost of energy storage, demand response
The interaction cost of the scheduling cost of lotus, micro-capacitance sensor and power distribution network constructs objective function；
S1, constraint condition is generated, generates output power constraint, the institute of the corresponding controlled distribution formula power supply of the objective function
State charge power constraint, discharge power constraint, scheduling whole story capacity-constrained, the residual capacity constraint, the demand response of energy storage
Total power demand of load, micro-capacitance sensor constrain the actual schedule power of demand response load, auxiliary variable, micro-capacitance sensor and distribution
Interaction power-balance constraint, micro-capacitance sensor between net buy power constraint to power distribution network, sell power constraint；
S2, approximate optimal solution is solved, objective function and variable are corresponding under the constraint condition by PSO Algorithm
Approximate optimal solution, the variable include the charging function of the output power of controlled distribution formula power supply, energy storage inverter exchange side input
The charge and discharge of rate and the discharge power of output, auxiliary variable, micro-capacitance sensor to the power distribution network power bought and the power of sale, energy storage
State, micro-capacitance sensor are to the actual schedule power of demand response load, micro-capacitance sensor into the purchase sale of electricity state of power distribution network, micro-capacitance sensor
Conventional load power and photovoltaic output power.
2. a kind of power dispatching method as described in claim 1, it is characterised in that: in step so), the objective function tool
Body are as follows:
Wherein, t is period, NTFor dispatching cycle, CGIt (t) is the cost of electricity-generating of controlled distribution formula power supply, CS(t) filling for energy storage
Electric discharge cost, CDRIt (t) is the scheduling cost of demand response load, CMIt (t) is the interaction cost of micro-capacitance sensor and power distribution network.
3. a kind of power dispatching method as claimed in claim 2, it is characterised in that: in step so), the controlled distribution formula
The cost of electricity-generating of power supply, the charge and discharge cost of the energy storage, the scheduling cost of the demand response load, the micro-capacitance sensor and match
The interaction cost of power grid is successively specifically:
CG(t)=[aPG(t)+b]Δt
Wherein, CGIt (t) is cost of electricity-generating of the controlled distribution formula power supply in the t period, a, b are cost coefficient, PG(t) controllable for the t period
The output power of distributed generation resource, Δ t are scheduling step-length；
Wherein, CSIt (t) is the average charge and discharge cost of energy storage t period within the investment payback time, KSFor the unit charge and discharge after conversion
Cost,WithThe respectively electric discharge function of the charge power of t period energy storage inverter exchange side input and output
Rate, η are the efficiency for charge-discharge of energy-storage units；
CDR(t)=KDR[PDR1(t)+PDR2(t)]Δt
Wherein, CDR(t) the scheduling cost for the demand response load paid needed for the t period for micro-capacitance sensor, KDRIt is negative for demand response
The unit of lotus dispatches cost, PDR1(t)、PDR2It (t) is auxiliary variable；
Wherein, CM(t) in the interaction cost of t period micro-capacitance sensor and power distribution network, λ (t) is the day-ahead power market electricity price of power distribution network,Respectively power of the t period micro-capacitance sensor to the power distribution network power bought and sale.
4. a kind of power dispatching method as claimed in claim 3, it is characterised in that: in step sl, the controlled distribution formula
The output power constraint of power supply, the output power of the controlled distribution formula power supply are constrained, the constraint of the charge power of the energy storage, are put
Electrical power constraint, scheduling whole story capacity-constrained, residual capacity constraint, total power demand of the demand response load, micro-capacitance sensor
About to the interaction power-balance between the actual schedule power of demand response load, auxiliary variable constraint, micro-capacitance sensor and power distribution network
Beam, micro-capacitance sensor buy power constraint to power distribution network, sell power constraint successively specifically:
Wherein,The peak power output and minimum output power of the respectively described controlled distribution formula power supply；
Wherein,For the maximum charge-discharge electric power that energy storage allows, USIt (t) is the charging and discharging state of energy storage, ES(0) exist for energy storage
The capacity of initial time is dispatched,The respectively greatest residual capacity and minimum that allow in scheduling process of energy storage
Residual capacity；
Wherein, PDRIt (t) is actual schedule power of the t moment micro-capacitance sensor to demand response load, DDRIt is being adjusted for demand response load
Total power demand in the period is spent,Respectively maximum power demand of the demand response load in t moment
With minimum power demand；
PDR1(t)≥0,PDR2(t)≥0
Wherein,For the expectation electric power of t moment demand response load；
Wherein, PL(t)、PPV(t) it is respectively conventional load power and photovoltaic output power in t moment micro-capacitance sensor；
Wherein,The respectively maximum value of micro-capacitance sensor and power distribution network exchange power, UM(t) it is sold for purchase of the micro-capacitance sensor to power distribution network
Electricity condition.
5. a kind of power dispatching method as described in claim 1, it is characterised in that: in step s 2, pass through particle swarm algorithm
Generate two populations, the optimization formula of the first population are as follows:
The optimization formula of second population are as follows:
Wherein, k is the number of iterations, and w is inertial factor, and rand () is the random number between 0 to 1, c1、c2For Studying factors,For the current location of particle i d dimension in kth time iteration, pbest is that each particle is gone through after the second population kth time iteration
The average value of history optimal value, gbest are the optimal value under group's optimal value of two populations is compared.
6. a kind of power dispatching method as claimed in claim 5, it is characterised in that: specifically include following step in step s 2
It is rapid:
S21, initialization population determine the size of the first population and the second population, and determine initial power:
Wherein, CiFor the initial day operation cost of i-th of particle,Respectively CiUpper and lower bound, βiFor position
Random number between 0 to 1；
Particle is considered as loose PQ node by S22, Load flow calculation, carries out Load flow calculation to micro-capacitance sensor；
S23, fitness function value is calculated, calculates each particle history optimal value of the first population, and calculate the group of the first population
Body optimal value calculates each particle history optimal value of the second population, and calculates group's optimal value of the second population；
The particle of first population is substituted into the optimization formula of first population, after obtaining optimization by S24, calculation optimization new particle
New particle, calculate the second population in each particle history optimal value mean value as pbest, take the group of first population
Optimal value and group's optimal value of second population substitute into the excellent of second population as gbest, and by corresponding particle
Change formula, each particle of each population is carried out range constraint by the new particle after being optimized；
S25, result judgement, judge whether to meet termination condition, are, terminate iteration, otherwise go to step S22.
7. a kind of electronic equipment, characterized by comprising: processor；
Memory；And program, wherein described program is stored in the memory, and is configured to be held by processor
Row, described program includes for executing the method as described in claim 1.
8. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that: the computer program quilt
Processor executes the method as described in claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811642329.2A CN109494813A (en) | 2018-12-29 | 2018-12-29 | A kind of power dispatching method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811642329.2A CN109494813A (en) | 2018-12-29 | 2018-12-29 | A kind of power dispatching method, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109494813A true CN109494813A (en) | 2019-03-19 |
Family
ID=65713413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811642329.2A Pending CN109494813A (en) | 2018-12-29 | 2018-12-29 | A kind of power dispatching method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109494813A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110350523A (en) * | 2019-07-10 | 2019-10-18 | 佳源科技有限公司 | Multi-energy complementation Optimization Scheduling based on demand response |
| CN110932257A (en) * | 2019-08-29 | 2020-03-27 | 昆明理工大学 | Micro-grid energy scheduling method |
| CN113335127A (en) * | 2021-05-14 | 2021-09-03 | 南方电网电动汽车服务有限公司 | Charging load scheduling method and device, computer equipment and storage medium |
| CN115085229A (en) * | 2022-06-29 | 2022-09-20 | 燕山大学 | Energy management method of hydrogen-containing energy storage microgrid |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140277599A1 (en) * | 2013-03-13 | 2014-09-18 | Oracle International Corporation | Innovative Approach to Distributed Energy Resource Scheduling |
| CN106451550A (en) * | 2016-11-10 | 2017-02-22 | 三峡大学 | A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms |
| CN107392395A (en) * | 2017-08-23 | 2017-11-24 | 天津大学 | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism |
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN108667147A (en) * | 2018-06-15 | 2018-10-16 | 贵州电网有限责任公司 | Straightening stream distribution center Optimization Scheduling in a kind of flexibility containing more micro-capacitance sensors |
-
2018
- 2018-12-29 CN CN201811642329.2A patent/CN109494813A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140277599A1 (en) * | 2013-03-13 | 2014-09-18 | Oracle International Corporation | Innovative Approach to Distributed Energy Resource Scheduling |
| CN106451550A (en) * | 2016-11-10 | 2017-02-22 | 三峡大学 | A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms |
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN107392395A (en) * | 2017-08-23 | 2017-11-24 | 天津大学 | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism |
| CN108667147A (en) * | 2018-06-15 | 2018-10-16 | 贵州电网有限责任公司 | Straightening stream distribution center Optimization Scheduling in a kind of flexibility containing more micro-capacitance sensors |
Non-Patent Citations (2)
| Title |
|---|
| 付蓉: "基于改进粒子群算法的微电网多时段经济调度", 《电力需求侧管理》 * |
| 刘一欣: "微电网两阶段鲁棒优化经济调度方法", 《中国电机工程学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110350523A (en) * | 2019-07-10 | 2019-10-18 | 佳源科技有限公司 | Multi-energy complementation Optimization Scheduling based on demand response |
| CN110932257A (en) * | 2019-08-29 | 2020-03-27 | 昆明理工大学 | Micro-grid energy scheduling method |
| CN113335127A (en) * | 2021-05-14 | 2021-09-03 | 南方电网电动汽车服务有限公司 | Charging load scheduling method and device, computer equipment and storage medium |
| CN115085229A (en) * | 2022-06-29 | 2022-09-20 | 燕山大学 | Energy management method of hydrogen-containing energy storage microgrid |
| CN115085229B (en) * | 2022-06-29 | 2022-12-30 | 燕山大学 | Energy management method for hydrogen-containing energy storage microgrid |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110689189B (en) | Combined cooling, heating and power supply and demand balance optimization scheduling method considering energy supply side and demand side | |
| CN104135025B (en) | Microgrid connection economic optimization method based on fuzzy particle swarm algorithm | |
| CN104362677B (en) | A kind of active distribution network distributes structure and its collocation method rationally | |
| CN105811409B (en) | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile | |
| CN103346562B (en) | Multi-time scale microgrid energy control method considering demand response | |
| CN102710013B (en) | Park energy-network energy optimizing management system based on microgrids and implementing method thereof | |
| CN107958300A (en) | A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response | |
| CN109327042A (en) | A kind of micro-grid multi-energy joint optimal operation method | |
| CN105990838B (en) | A kind of island microgrid Optimization Scheduling of meter and schedulable and translatable load | |
| CN109494813A (en) | A kind of power dispatching method, electronic equipment and storage medium | |
| CN106026152A (en) | Charging and discharging scheduling method for electric vehicles connected to micro-grid | |
| CN106058855A (en) | Active power distribution network multi-target optimization scheduling method of coordinating stored energy and flexible load | |
| CN106451552B (en) | A kind of microgrid energy management system distributed optimization algorithm based on gesture game | |
| CN109740786A (en) | A kind of multiterminal flexible interconnection distribution network planning method and system | |
| CN104767224A (en) | Energy management method of multi-energy-storage-type containing grid-connection type wind and light storage micro-grid | |
| CN105787605A (en) | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm | |
| CN109217290A (en) | Meter and the microgrid energy optimum management method of electric car charge and discharge | |
| CN112800658A (en) | Active power distribution network scheduling method considering source storage load interaction | |
| CN105870950A (en) | Intelligent building adjustable resource multi-target coordination system and optimal control method | |
| CN113988444A (en) | Electricity charge optimization control system and method for light storage system | |
| CN107769236A (en) | A kind of wind power generating set energy-storing and power-generating system and its energy dispatching method | |
| CN105226689A (en) | Consider polymorphic type energy-storage system energy management method and the system of operation and maintenance | |
| CN108039722A (en) | A kind of distribution type renewable energy system Optimal Configuration Method suitable for alternating current-direct current mixing | |
| CN110112734A (en) | A kind of microgrid Optimization Scheduling based on heuristic rule | |
| CN109245091A (en) | A kind of two_layer strategies dispatching method towards sale of electricity company |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190319 |