CN108964050A - Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response - Google Patents
Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response Download PDFInfo
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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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
- H02J3/28—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
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- H02J3/383—
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- H02J3/386—
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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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- 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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- 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
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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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Abstract
The invention discloses a kind of micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response, content include: micro-capacitance sensor Optimal Scheduling of the building based on Demand Side Response: micro-capacitance sensor Optimal Scheduling being divided into two subsystems: upper layer Optimized Operation subsystem and lower layer's Optimized Operation subsystem；Upper layer Optimal Operation Model is constructed for upper layer Optimized Operation subsystem and it is solved；Net load after adjusting can be explicitly indicated that out, and be used in lower layer's Optimized Operation subsystem；Lower layer's Optimal Operation Model is constructed for lower layer's Optimized Operation subsystem: introducing ADHDP algorithm to solve to lower layer's Optimal Operation Model.The superiority of the method for the present invention is verified using emulation experiment.The cost of electricity-generating that The present invention reduces micro-capacitance sensors in Optimized Operation guarantees the optimal control that energy storage device is realized while user benefit in sufficiently consumption distributed generation resource.The present invention provides for the economy that micro-capacitance sensor is dispatched with original scheduling strategy.
Description
Technical field
The invention belongs to the field of energy management of micro-capacitance sensor, and in particular to based on a variety of Demand Side Response resources and adaptively
The design method of the micro-capacitance sensor Optimized Operation strategy of dynamic programming algorithm.
Background technique
With clean energy resource is developed, ensure that energy security becomes the core content of energy field, distributed power generation gradually exists
Various countries rise, however distributed power generation has dispersibility, not manageability, therefore just have the appearance of micro-capacitance sensor.Micro-capacitance sensor can
High efficiency solves the problems, such as the extensive dispersion access of distributed generation resource, also can be used as the useful supplement of traditional power grid, is to make to be distributed
Formula power generation, which becomes traditional power grid, can receive the effective carrier using the energy.The economy and environment friendly of micro-capacitance sensor are to attract to use
Family can simultaneously obtain the key that society accepts extensively, and realize these targets of micro-capacitance sensor operation, not only need from energy supply angle,
According to the operation and energy consumption characteristics of various distributed energies, the Optimizing Allocation of the energy is studied, more sufficiently to excavate Demand-side
The potentiality of all kinds of resources allow user to play an active part in micro-capacitance sensor Optimized Operation.
The energy-optimised management of micro-capacitance sensor is studied now with very much, but existing research exists to the consideration of micro-capacitance sensor structure
Comprehensively, to the response of Demand-side resource do not understand deeply and to the not congruent problem of operating cost consideration.Related scholar proposes to examine
Consider the method for the microgrid energy optimum management of Demand-side, but only considered conventional requirement side resource i.e. will be including can be to electricity price
The burdened resource and interruptible load that signal or incentive mechanism respond do not account for the mode for improving Load adjustment structure,
User benefit is not accounted for, the overall effect of running optimizatin is promoted in terms of Demand-side and supply side interact.Related scholar is to wide
Adopted Demand-side resource includes that distributed power generation, energy storage resource and load these three types are studied, but are not examined in scheduling process
The problem of energy storage device efficiency for charge-discharge cached between " source-lotus " is without accurate model is served as in worry, guarantees the economy of cost of electricity-generating
The optimal control of property and energy storage device.Therefore, the present invention proposes that dual-layer optimization dispatches mould in the design aspect of operation plan a few days ago
Type, upper layer scheduling model improve tou power price mode and consider that user satisfaction interacts to adjust load with distributed generation resource, under
Layer scheduling model is then solved using dependence heuristic dynamic programming (ADHDP) algorithm is executed, to solve above-mentioned energy storage device
The problem of, reduce cost of electricity-generating.
Summary of the invention
The present invention provides a kind of micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response, and the purpose is to solve sufficiently
Using the characteristic of Demand-side resource, the economy of micro-capacitance sensor Optimal Operation Model is improved while meeting user power utilization load.
The present invention is based on the responses of Demand-side resource, construct dual-layer optimization scheduling model, introduce and improve in the Optimal Operation Model of upper layer
Tou power price and consider user satisfaction；ADHDP algorithm is introduced in the solution of lower layer's Optimal Operation Model, does not depend on nothing especially
The energy-storage system of accurate model, so that scheduling cost is reduced.
In order to achieve the above objectives, the present invention is achieved by the following technical solutions:
A kind of micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response, particular content include the following steps:
(1) the micro-capacitance sensor Optimal Scheduling based on Demand Side Response is constructed:
Micro-capacitance sensor Optimal Scheduling is divided into two subsystems: upper layer Optimized Operation subsystem and lower layer's Optimized Operation
System；In the Optimized Operation subsystem of upper layer, micro-capacitance sensor by improved tou power price mode adjust user side load power come
Improve the net load between distributed generation resource and load；In lower layer's Optimized Operation subsystem, with upper layer Optimized Operation subsystem
Obtained in net load as constraint condition, and consider without accurate charge and discharge energy-storage system model, by being directed to its Demand-side
The optimization algorithm of resources characteristic is come so that dispatching that cost is minimum, and thus obtained optimal solution is as micro-capacitance sensor Optimized Operation scheme；
(2) upper layer Optimal Operation Model is constructed for upper layer Optimized Operation subsystem and it is solved:
In the Optimal Operation Model of upper layer, based on a few days ago to wind-powered electricity generation, photovoltaic power output and load in the predicted value of day part, adopt
With improved tou power price strategy, while user satisfaction is considered, so that between upper layer Optimized Operation subsystem day part source-lotus
The quadratic sum of net load be minimum, net load curve is gentle, and carries out quick optimizing solution using particle swarm algorithm, after adjusting
Net load can be explicitly indicated that out, and in lower layer's Optimized Operation subsystem；
(3) lower layer's Optimal Operation Model is constructed for lower layer's Optimized Operation subsystem:
In micro-capacitance sensor lower layer Optimized Operation subsystem, using the net load that upper layer Optimal Operation Model obtains as constraint item
Part carries out to construct power-balance formula to energy-storage system, interruptible load and with three kinds of resources of interaction power of bulk power grid
Rational management, building is with lower layer's Optimized Operation subsystem power supply cost at least for lower layer's Optimal Operation Model of objective function；Needle
It is complicated to energy-storage system charge and discharge process, it is difficult to its accurate charging and recharging model to be obtained, therefore, using being not relying on accurate control
The ADHDP algorithm of object model solves it；
(4) lower layer's Optimal Operation Model is solved using ADHDP algorithm:
Present invention introduces ADHDP algorithms to solve to lower layer's Optimal Operation Model, first optimize micro-capacitance sensor lower layer
The discrete-time system model that scheduling model is considered as limited period of time scheduling is converted；Secondly, Training valuation network and holding
Row network；Go over daily scheduling cost data according to micro-capacitance sensor and train neural network, adjust network weight, hides the number of plies
Mesh, Studying factors assess the scheduling cost in system certain time well；Then by the scheduling a few days ago of micro-capacitance sensor
Data are updated in Auto-adapted plan algorithm, and iteration optimizing obtains optimal solution；
(5) superiority of the method is verified using emulation experiment.
Novelty of the invention and have the beneficial effect that the following aspects:
(1) in order to the more well arranged and abundant consumption distributed energy of dispatching requirement side resource, upper layer optimization is adjusted
Spend model with the minimum objective function of the sum of day part net load square, increase the interaction between source-lotus, and by adjusting after
Net load value connected with lower layer Optimal Operation Model.
(2) different from traditional tou power price mode, in the Optimal Operation Model of upper layer, with improved tou power price mode
Can accurately indicate adjust after load, and guarantee user can play an active part in Demand Side Response and adjust after electricity price not
The interests of user can be damaged.
(3) it when being solved to micro-capacitance sensor lower layer Optimal Operation Model, uses and executes dependence heuristic dynamic programming
(ADHDP) algorithm, used optimization algorithm before being different from；ADHDP algorithm can estimate the use of energy-storage system for micro-
The influence of operation of power networks cost reduces operating cost so that guaranteeing the higher efficiency for charge-discharge of energy-storage system in Optimized Operation.
The cost of electricity-generating that The present invention reduces micro-capacitance sensors in Optimized Operation guarantees to use in sufficiently consumption distributed generation resource
The optimal control of energy storage device is realized while the interests of family.Its key point is to dispatch using Demand-side resource for micro-capacitance sensor
The problem of, dual-layer optimization scheduling strategy is constructed, in the Optimized Operation of upper layer, micro-capacitance sensor uses improved tou power price
Strategy, while considering user satisfaction, come so that the net load between micro-capacitance sensor Optimal Scheduling source-lotus is minimum；Under
In layer Optimized Operation, introduces ADHDP and lower layer's Optimal Operation Model algorithm is solved, realize the optimal control to energy storage device, make
Cost reduction must be dispatched.The present invention provides for the economy that micro-capacitance sensor is dispatched with original scheduling strategy.
Detailed description of the invention
Fig. 1 is the micro-capacitance sensor dual-layer optimization scheduling flow figure based on Demand Side Response；
Fig. 2 is micro-capacitance sensor upper layer Optimized Operation structure chart；
Fig. 3 is micro-capacitance sensor lower layer Optimized Operation structure chart；
Fig. 4 is ADHDP algorithm structure schematic diagram.
Specific embodiment
Present invention is further described in detail with specific embodiment with reference to the accompanying drawing:
A kind of micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response of the invention, flow chart as shown in Figure 1,
The method particular content includes the following steps:
Step 1: micro-capacitance sensor Optimal Scheduling of the building based on Demand Side Response:
Micro-capacitance sensor Optimal Scheduling is divided into two subsystems: upper layer Optimized Operation subsystem and lower layer's Optimized Operation
System；In the Optimized Operation subsystem of upper layer, micro-capacitance sensor improves distributed generation resource by adjusting user side load power and bears
Net load between lotus；In lower layer's Optimized Operation subsystem, with the work of net load obtained in the Optimized Operation subsystem of upper layer
For constraint condition, and consider to pass through the optimization algorithm for being directed to its Demand-side resources characteristic without accurate charge and discharge energy-storage system model
Come so that dispatching that cost is minimum, and thus obtained optimal solution is as micro-capacitance sensor Optimized Operation scheme.
Construct micro-capacitance sensor dual-layer optimization scheduling model, including upper layer Optimal Operation Model and lower layer's Optimal Operation Model.?
In the Optimal Operation Model of upper layer, according to the prediction of load and distributed energy power generation, improved tou power price mechanism is proposed to draw
User response photovoltaic, wind power output variation are led, the net load change of day part is mitigated, improves system wind-powered electricity generation, photovoltaic dissolves energy
Power；In lower layer's Optimal Operation Model, by rational allocation interruptible load and energy storage, building is with lower layer's Optimized Operation
System cost is minimised as source-net-lotus-storage Optimal Operation Model of objective function, while fully considering without accurate energy storage device
Efficiency for charge-discharge model, rely on heuristic dynamic programming algorithm using executing and avoid this problem, while being sought by iteration
Excellent process obtains optimal scheduling strategy.However, the net load that upper layer Optimal Operation Model obtains is as lower layer's Optimal Operation Model
Power-balance constraint condition, particular content will be illustrated in step 3.Demand-side resource, that is, electricity price type load, can in
Disconnected load and energy-storage system have obtained rationally and adequately calling respectively in the Optimized Operation subsystem of the upper and lower.
Step 2: upper layer Optimal Operation Model is constructed for upper layer Optimized Operation subsystem and it is solved:
As shown in Fig. 2, in the Optimal Operation Model of upper layer, based on a few days ago to wind-powered electricity generation, photovoltaic power output and load in day part
Predicted value, using improved tou power price strategy, while user satisfaction is considered, when so that upper layer Optimized Operation subsystem is each
The quadratic sum of net load between Duan Yuan-lotus is minimum, and net load curve is gentle, and carries out quick optimizing using particle swarm algorithm
It solves, the net load after adjusting can be explicitly indicated that out, and in lower layer's Optimized Operation subsystem；Step is embodied in it
It is rapid as follows:
The characteristic, photovoltaic power output and the inconsistent situation of load peak interval of time that peak is demodulated for wind-powered electricity generation, are rung based on Demand-side
It answers, establishes micro-capacitance sensor upper layer Optimal Operation Model；
Predicted value based on load and distributed energy power generation, proposition guide user to ring with improved tou power price mechanism
Answer photovoltaic, wind power output variation, mitigate the net load change of day part, improve the upper layer Optimized Operation subsystem wind-powered electricity generation,
Photovoltaic digestion capability, building is with the net load between upper layer Optimized Operation subsystem source-lotus for the smallest objective function:
Wherein t=1,2 ..., 24 represent each of daily 24 hours hours as a response period；NSIt is scene
Number, PsIt is that each scene corresponding probability occurs；NW, NPVRespectively represent wind power generating set sum and solar components；Pnet
It (t) is net load power, PL(t) ' be adjust after load power.
Inequality constraints condition are as follows:
Pk.min≤Pk(t)≤Pk.max (3)
ms≥ms.min (4)
mp≥mp.min (5)
pmin≤p(t)≤pmax (7)
In formula, KpFor coefficient of making concessions, p0(t) electricity price before optimizing for the t period, pLoad0(t) optimize preload prediction for the t period
Value, p (t) are electricity price after optimizing the t period, pminFor the lower limit value of electricity price, pmaxFor the upper limit value of electricity price.
Upper layer Optimal Operation Model seeks optimal solution using the particle swarm algorithm in intelligent optimization algorithm, meets constraint item
When part, corresponding functional value very little makes total target function value minimum, to obtain upper layer Optimized Operation by iteration optimization
The optimal solution of model, specific steps do not repeat herein.
Illustrate the distributed generation resource used in the Optimal Operation Model of upper layer, tou power price and user satisfaction model below
It is respectively as follows:
1) wind energy conversion system model
In some period, the output power of each wind power generating set is represented by formula (8):
2) photovoltaic system model
In a certain period, the output power of photovoltaic generating system is represented by formula (9):
3) TOU Power Price Model
Valuable source of the electricity price type load as Demand Side Response, it should adjust its work of embodiment by more reasonable
With；Tou power price is just particularly important as important regulating measure, its improvement；Demand Elasticity Coefficient is the change of demand
Ratio between amount and price knots modification, it includes from Demand Elasticity Coefficient and coefficient of cross elasticity；Wherein, from demand elasticity system
Number is expressed as follows:
Wherein,After representing tou power price, the ratio of the knots modification of i period load and former load,Represent valence
The ratio of the knots modification of lattice and original price；
Coefficient of cross elasticity represents the relationship of the change of i period load caused by j period electricity price changes, it is expressed as follows:
A middle part throttle characteristics is analyzed, the Demand Elasticity Coefficient of available each period, forms demand elasticity square
Battle array；Load after adjusting can be acquired by new electricity price, be expressed as follows:
Wherein, PLoad0It (i) is i period original load power, PL(i) ' it is power after adjusting the i period, p0It (i) is the i period
Electricity price originally, p (i) are the electricity prices of i period after optimization；
4) user satisfaction model
User satisfaction has highly important influence from the point of view of in power marketing, on the result quality of tou power price response.
It only fully considers the satisfaction of user, optimum results could be made neither to damage the interests of user, so that user is pleased oneself, again
It can achieve the purpose that coordinates user electricity consumption adapts to wind-powered electricity generation, grid-connected operation.The method of the present invention is referred to using two user satisfaction
Mark, i.e. user power utilization mode Satisfaction index and demand charge pay Satisfaction index, and using them as Optimal Operation Model
In constraint condition.
Power mode satisfaction msMathematic(al) representation are as follows:
In formula:For the sum of the knots modification absolute value of each period electricity in optimization front and back；To optimize preceding total use
Electricity.
Electric cost expenditure satisfaction mpMathematic(al) representation are as follows:
In formula: Δ CtFor the variable quantity summation of each period demand charge expenditure in optimization front and back；CtIt is each to optimize preceding user
The expenditure summation of the period electricity charge.
msAnd mpValue it is bigger, show that user satisfaction level is higher.
Step 3: lower layer's Optimal Operation Model is constructed for lower layer's Optimized Operation subsystem:
As shown in figure 3, in micro-capacitance sensor lower layer Optimized Operation subsystem, the net load that is obtained with upper layer Optimal Operation Model
Construct power-balance formula as constraint condition, and to energy-storage system, interruptible load and with the interaction power three of bulk power grid
Kind resource carries out rational management, and building, which is at least optimized with lower layer's Optimized Operation subsystem power supply cost for the lower layer of objective function, adjusts
Spend model；It is complicated for energy-storage system charge and discharge process, it is difficult to obtain its accurate charging and recharging model, therefore, using and disobey
The ADHDP algorithm of accurate control object model is relied to solve it；Its specific implementation step is as follows:
Source-net-lotus-storage Optimal Operation Model be wind-powered electricity generation, photovoltaic renewable energy power generation, with the interaction power of bulk power grid,
Interruptible load and energy storage device multiple resources power output model, consider the method for operation of energy storage device, construct with micro-capacitance sensor lower layer
The objective function of Optimized Operation subsystem cost minimization:
Pgrid(t)+PB(t)+Psl(t)=Pnet(t) (16)
C in formulagridIt (t) is the electricity price of t period bulk power grid, PgridIt (t) is the interaction power of t period micro-capacitance sensor and bulk power grid,
FslFor interruptible load response cost, PslIt (t) is interruptible load responding power, FbatFor energy storage device charge and discharge cost, E
It (t) is the state-of-charge of t period energy storage device, PBIt (t) is the output power of t period energy storage device,Respectively micro- electricity
Net interacts the minimum value and maximum value of power with bulk power grid；
Pnet>=0, Pnet(t)=Pgrid(t)+PBD(t)-PGB(t)+Psl(t) (18)
In formula, PBD(t) it is exported by battery to the power of load, P for the t periodGB(t),PRBIt (t) is respectively by main power grid
To the charge power of energy storage device, from micro-capacitance sensor to the charge power of energy storage device；
1) battery model
Energy-storage system of accumulator is particularly significant as stable operation of the General Requirement side resource to micro-capacitance sensor, this has benefited from it
Both it can make load, power supply can also be done.To increase accumulator cell charging and discharging efficiency and service lifetime of accumulator, need to consider following pact
Beam condition:
Battery capacity constraint condition can avoid accumulator super-charge or over-discharge, constraint condition are expressed as follows:
The storage volume of battery is expressed as follows:
EB(t+1)=EB(t)-PB(t)η(PB(t)) (23)
η(PB(t))=0.898-0.173 | PB(t)|/Prate (24)
Wherein,WithIt is the minimum value and maximum value of accumulator cell charging and discharging power；EBIt (t) is t period battery
Energy is stored, η is the efficiency for charge-discharge of battery, and Δ t is in response to the period；PrateIt is the rated power of battery；
3) interruptible load model
Interruptible load is responsible for by Demand Side Response management organization, and rationally utilization can significantly reduce costs higher unit
Start and stop, response time can freely regulate and control, and model construction is expressed as follows:
In formula, [αj,βj] it is the maximum magnitude that jth item interruptible load allows to respond,WithRespectively jth Xiang Kezhong
The beginning and ending time of disconnected load responding, yslj(t) working condition of jth item interruptible load is indicated, 1 indicates that response is interrupted, and 0 indicates
Interruption, P are not respondedsljIndicate the rated power of jth item interruptible load.
Step 4: lower layer's Optimal Operation Model is solved using ADHDP algorithm:
As shown in figure 4, present invention introduces ADHDP algorithms to solve to lower layer's Optimal Operation Model, first by micro- electricity
The discrete-time system model that layer Optimal Operation Model off the net is considered as limited period of time scheduling is converted；Secondly, training is commented
Estimate network and executes network；Go over daily scheduling cost data according to micro-capacitance sensor and train neural network, adjusts network weight,
Number of layers is hidden, Studying factors assess the scheduling cost in system certain time well；Then by micro-capacitance sensor
Scheduling data are updated in Auto-adapted plan algorithm a few days ago, and iteration optimizing obtains optimal solution；Its specific implementation step is as follows:
Micro-capacitance sensor Optimal Scheduling is considered as the discrete time nonlinear dynamic system in finite time section, and dynamic is advised
The method of drawing is a kind of method of research multistage decision optimization, so that current decision is optimal for overall goals, but is actually being answered
The problem of will appear " dimension calamity " in, therefore propose adaptive dynamic programming method.Its thought is to utilize approximation to function knot
Structure approaches performance index function and control strategy in Dynamic Programming Equation by progressive alternate, and then gradually approaches non-linear
The optimal control solution of system.
For energy storage device charge-discharge velocity, due to being difficult to obtain lotus of its accurate mathematical model to calculate energy storage device
Electricity condition (storage grade), therefore the method for the present invention relies on starting formula dynamic programming algorithm using execution neural network based
(ADHDP) this method does not need prototype network to predict the system mode of subsequent time, only includes evaluation network and execution net
Network can reduce the dependence for executing network for system model, suitable for being difficult to the case where obtaining the mathematical models of system.
The discrete system state variable of N number of period is defined as follows:
X (t+1)=ft(x(t),u(t),t) (28)
In formula, x (t) is the state variable of system t moment, indicates that the state-of-charge of current t moment energy storage device, u (t) are
The control variable of t moment, including current t moment net load power and the interaction power of bulk power grid and the interruptible load of calling
General power.
In ADHDP algorithm, x (t) and u (t) are the input for evaluating network, performance index function corresponding with the system
Is defined as:
Wherein U (x (t), u (t), t) is utility function, is expressed as the scheduling cost of current t period, γ be discount because
Son, and 0≤γ≤1 evaluate the output of networkFor approximated cost function J, realized by minimizing error below；
In formula,Wherein WcTo evaluate network parameter.As the E for all periodsc(t)=0
When, it is clear that haveTherefore error function is minimized, a trained neural network will be obtained, export estimating for J
Meter.
1) micro-capacitance sensor lower layer Optimal Operation Model is converted
Micro-capacitance sensor scheduling includes 24 scheduling slots.In each period, with the charged shape of battery t period initial time
State is as state variable, then micro-capacitance sensor state variable: x (t)=E (t) makes system input variable u1(t)=Pgrid(t),u2(t)
=PBD(t)-PGB(t), u3(t)=Psl(t)。
Micro-capacitance sensor scheduling model control variable is expressed as:
U (t)=(Pgrid(t),PBD(t)-PGB(t),Psl(t)) (31)
The state variable of lower layer's Optimized Operation subsystem subsequent time indicates are as follows:
X (t+1)=f (x (t), u (t), t)=x (t)-(u2(t)-PRB(t))η(u2(t)-PRB(t)) (32)
Its utility function is defined as:
U (x (t), u (t), t)=Cgrid(t)u1(t)+F(u3(t))+Cbat(x(t+1)-x(t)) (33)
In formula, CbatFor the amortization charge of energy storage device charge and discharge per unit electric energy.
Objective function is rewritten as performance index function:
In formula, μ (t)=(u (t), u (t+1) ... u (23)) represents control sequence from the t period to the 23rd period.
Optimal performance index function representation are as follows: J*(x (t), t)=min { J (x (t), μ (t), t) }.
2) iteration searching process
The training that network is first completed to evaluation network and executed before optimal cost characteristic index function, obtains two networks
Two network weights that hidden layer number of nodes, learning rate, discount factor and update appropriate are adjusted.
The iteration cycle process of optimal cost characteristic index function includes outer loop and internal circulation, and outer loop is to optimization
Performance index function is compared, and chooses any positive definite integral form Φ (x (0), u (0)) as initial performance target function:
J0(x (0), μ (0))=Φ (x (0), u (0)) (35)
Initial control sequence indicates are as follows:
Performance index function updates are as follows:
J1(x (0), μ (0)=J (x (0), μ0(0)) (37)
Work as i=1, when 2 ..., outer loop iteration between formula (38) and formula (39):
Ji+1(x (0), μ (0)=J (x (0), μi(0)) (39)
If following formula (40) is set up, outer loop stops.
||Ji+1(x(0),μi+1(0))-Ji(x (0), μi(0))||≤ε (40)
In formula, ε is computational accuracy.
The cost function that outer loop is optimized, however control sequence cannot directly obtain, and need to carry out internal circulation
To obtain control sequence.
Here define l be inner iterative number, l=0,1 ..., 23.I=0., when l=0, initial performance target function table
It is shown as:
Work as i=0, l=0,1 ..., when 23, inside, which circulates between (43) the two update operations of following formula (42) and formula, to be connected
Continuous iteration:
For any i=1,2 ..., j=0, when 1 ..., 23, inside circulates in following formula (44) and formula (45) two updates
Subsequent iteration between operation:
Inside circulation is to obtain the control variable of each period, therefore the number of iterations is determining；Outer loop is
In order to obtain optimal cost value, therefore the number of iterations is uncertain.
Step 5: the superiority of the method is verified using emulation experiment.
By consulting related data, master data information needed for obtaining simulating, verifying.Pass through the optimization with corresponding model
Dispatching method is compared, while user benefit can be guaranteed by sufficiently proving the dual-layer optimization scheduling strategy of micro-capacitance sensor, sufficiently
Distributed energy is dissolved, so that micro-capacitance sensor scheduling cost is smaller.
As it will be easily appreciated by one skilled in the art that the foregoing is merely preferred embodiments of the present invention, not to
The limitation present invention, all any modifications, equivalent replacements, and improvements etc. done within the spirit and principles in the present invention should all wrap
Containing within protection scope of the present invention.
Claims (1)
1. a kind of micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response, the method particular content includes following step
It is rapid:
Step 1: micro-capacitance sensor Optimal Scheduling of the building based on Demand Side Response
Micro-capacitance sensor Optimal Scheduling is divided into two subsystems: upper layer Optimized Operation subsystem and lower layer's Optimized Operation subsystem
System；In the Optimized Operation subsystem of upper layer, micro-capacitance sensor adjusts user side load power by improved tou power price mode to change
Net load between kind distributed generation resource and load；In lower layer's Optimized Operation subsystem, in the Optimized Operation subsystem of upper layer
Net load obtained considers as constraint condition without accurate charge and discharge energy-storage system model, by providing for its Demand-side
The optimization algorithm of source feature is come so that dispatching that cost is minimum, and thus obtained optimal solution is as micro-capacitance sensor Optimized Operation scheme；
Step 2: upper layer Optimal Operation Model is constructed for upper layer Optimized Operation subsystem and it is solved:
In the Optimal Operation Model of upper layer, based on a few days ago to wind-powered electricity generation, photovoltaic power output and load day part predicted value, using changing
Into tou power price strategy, while user satisfaction is considered, so that net between upper layer Optimized Operation subsystem day part source-lotus
The quadratic sum of load is minimum, and net load curve is gentle, and carries out quick optimizing solution using particle swarm algorithm, net after adjusting
Load can be explicitly indicated that out, and be used in lower layer's Optimized Operation subsystem；Its specific implementation step is as follows:
The characteristic, photovoltaic power output and the inconsistent situation of load peak interval of time that peak is demodulated for wind-powered electricity generation, are based on Demand Side Response, build
Vertical micro-capacitance sensor upper layer Optimal Operation Model；
Predicted value based on load and distributed energy power generation proposes to guide user response light with improved tou power price mechanism
Volt, wind power output variation, mitigate the net load change of day part, improve the upper layer Optimized Operation subsystem wind-powered electricity generation, photovoltaic
Digestion capability, building is with the net load between upper layer Optimized Operation subsystem source-lotus for the smallest objective function:
Wherein t=1,2 ..., 24 represent each of daily 24 hours hours as a response period；NSIt is scene number, Ps
It is that each scene corresponding probability occurs；NW, NPVRespectively represent wind power generating set sum and solar components；PnetIt (t) is net
Load power, PL(t) ' be adjust after load power；
Inequality constraints condition are as follows:
Pk.min≤Pk(t)≤Pk.max (3)
ms≥ms.min (4)
mp≥mp.min (5)
pmin≤p(t)≤pmax (7)
In formula, KpFor coefficient of making concessions, p0(t) electricity price before optimizing for the t period, pLoad0(t) optimize preload predicted value, p for the t period
(t) electricity price after optimizing for the t period, pminFor the lower limit value of electricity price, pmaxFor the upper limit value of electricity price；
Upper layer Optimal Operation Model seeks optimal solution using the particle swarm algorithm in intelligent optimization algorithm, meets constraint condition
When, corresponding functional value very little makes total target function value minimum, to obtain upper layer Optimized Operation mould by iteration optimization
The optimal solution of type；
Distributed generation resource, tou power price and the user satisfaction model used in the Optimal Operation Model of upper layer are respectively as follows:
1) wind energy conversion system model
In some period, the output power of each wind power generating set is represented by formula (8):
2) photovoltaic system model
In a certain period, the output power of photovoltaic generating system is represented by formula (9):
3) TOU Power Price Model
Valuable source of the electricity price type load as Demand Side Response, it should adjust its effect of embodiment by more reasonable；Point
When electricity price be just particularly important as important regulating measure, its improvement；Demand Elasticity Coefficient be demand knots modification with
Ratio between price knots modification, it includes from Demand Elasticity Coefficient and coefficient of cross elasticity；Wherein, from Demand Elasticity Coefficient table
Show as follows:
Wherein,After representing tou power price, the ratio of the knots modification of i period load and former load,Represent changing for price
The ratio of variable and original price；
Coefficient of cross elasticity represents the relationship of the change of i period load caused by j period electricity price changes, it is expressed as follows:
A middle part throttle characteristics is analyzed, the Demand Elasticity Coefficient of available each period, forms demand elasticity matrix；By
New electricity price can acquire the load after adjusting, and be expressed as follows:
Wherein, PLoad0It (i) is i period original load power, PL(i) ' it is power after adjusting the i period, p0It (i) is that the i period is original
Electricity price, p (i) are the electricity prices of i period after optimization；
4) user satisfaction model
User satisfaction has highly important influence from the point of view of in power marketing, on the result quality of tou power price response；Only
It fully considers the satisfaction of user, optimum results could be made neither to damage the interests of user, user is made to please oneself, and can be reached
The purpose of wind-powered electricity generation, grid-connected operation is adapted to coordinates user electricity consumption；Here two user satisfaction indexs are used, i.e. user uses
Electric mode Satisfaction index and demand charge pay Satisfaction index, and using them as the constraint item in Optimal Operation Model
Part；
Power mode satisfaction msMathematic(al) representation are as follows:
In formula:For the sum of the knots modification absolute value of each period electricity in optimization front and back；To optimize preceding total electricity consumption
Amount；
Electric cost expenditure satisfaction mpMathematic(al) representation are as follows:
In formula: Δ CtFor the variable quantity summation of each period demand charge expenditure in optimization front and back；CtTo optimize preceding user's each period
The expenditure summation of the electricity charge；
msAnd mpValue it is bigger, show that user satisfaction level is higher；
Step 3: lower layer's Optimal Operation Model is constructed for lower layer's Optimized Operation subsystem:
In micro-capacitance sensor lower layer Optimized Operation subsystem, come using the net load that upper layer Optimal Operation Model obtains as constraint condition
Power-balance formula is constructed, and is carried out rationally to energy-storage system, interruptible load and with three kinds of resources of interaction power of bulk power grid
Scheduling, building is with micro-capacitance sensor Optimal Scheduling power supply cost at least for lower layer's Optimal Operation Model of objective function；For storage
Can system charge and discharge process it is complicated, it is difficult to its accurate charging and recharging model is obtained, therefore, using being not relying on accurate control object
The ADHDP algorithm of model solves it；Its specific implementation step is as follows:
Source-net-lotus-storage Optimal Operation Model be wind-powered electricity generation, photovoltaic renewable energy power generation, with the interaction power of bulk power grid, can in
Disconnected load and energy storage device multiple resources power output model, consider the method for operation of energy storage device, building is with the optimization of micro-capacitance sensor lower layer
The objective function of scheduler subsystem cost minimization:
Pgrid(t)+PB(t)+Psl(t)=Pnet(t) (16)
C in formulagridIt (t) is the electricity price of t period bulk power grid, PgridIt (t) is the interaction power of t period micro-capacitance sensor and bulk power grid, FslFor
Interruptible load response cost, PslIt (t) is interruptible load responding power, FbatFor energy storage device charge and discharge cost, E (t) is t
The state-of-charge of period energy storage device, PBIt (t) is the output power of t period energy storage device,Respectively micro-capacitance sensor and big
The minimum value and maximum value of power grid interaction power；
Pnet>=0, Pnet(t)=Pgrid(t)+PBD(t)-PGB(t)+Psl(t) (18)
In formula, PBD(t) it is exported by battery to the power of load, P for the t periodGB(t),PRBIt (t) is respectively from main power grid to storage
Can equipment charge power, from micro-capacitance sensor to the charge power of energy storage device；
1) battery model
Energy-storage system of accumulator is particularly significant as stable operation of the General Requirement side resource to micro-capacitance sensor, this, which has benefited from it, both may be used
Make load, power supply can also be done；To increase accumulator cell charging and discharging efficiency and service lifetime of accumulator, need to consider following constraint item
Part:
Battery capacity constraint condition can avoid accumulator super-charge or over-discharge, constraint condition are expressed as follows:
The storage volume of battery is expressed as follows:
EB(t+1)=EB(t)-PB(t)η(PB(t)) (23)
η(PB(t))=0.898-0.173 | PB(t)|/Prate (24)
Wherein,WithIt is the minimum value and maximum value of accumulator cell charging and discharging power；EB(t) be t period battery storage energy
Amount, η is the efficiency for charge-discharge of battery, and Δ t is in response to the period；PrateIt is the rated power of battery；
3) interruptible load model
Interruptible load is responsible for by Demand Side Response management organization, and rationally utilization can significantly reduce costs opening for higher unit
Stop, the response time can freely regulate and control, and model construction is expressed as follows:
In formula, [αj,βj] it is the maximum magnitude that jth item interruptible load allows to respond,WithRespectively jth item can interrupt negative
The beginning and ending time of lotus response, yslj(t) working condition of jth item interruptible load is indicated, 1 indicates that response is interrupted, and 0 indicates not ring
It should interrupt, PsljIndicate the rated power of jth item interruptible load；
Step 4: lower layer's Optimal Operation Model is solved using ADHDP algorithm:
ADHDP algorithm is introduced to solve to lower layer's Optimal Operation Model, first regards micro-capacitance sensor lower layer Optimal Operation Model
A discrete-time system model for limited period of time scheduling is converted；Secondly, Training valuation network and execution network；According to
Micro-capacitance sensor goes over daily scheduling cost data and trains neural network, adjusts network weight, hides number of layers, Studying factors,
Scheduling cost in system certain time is assessed well；Then the data of scheduling a few days ago of micro-capacitance sensor are updated to certainly
It adapts in planning algorithm, iteration optimizing obtains optimal solution；Its specific implementation step is as follows:
For energy storage device charge-discharge velocity, due to being difficult to obtain charged shape of its accurate mathematical model to calculate energy storage device
State, therefore prototype network is not needed using execution neural network based dependence this method of starting formula dynamic programming algorithm to predict
The system mode of subsequent time only includes evaluation network and executes network, can reduce execute network for system model according to
Rely, suitable for being difficult to the case where obtaining the mathematical models of system；
The discrete system state variable of N number of period is defined as follows:
X (t+1)=ft(x(t),u(t),t) (28)
In formula, x (t) is the state variable of system t moment, the state-of-charge of current t moment energy storage device is indicated, when u (t) is t
The control variable at quarter, including current t moment net load power and the interaction power of bulk power grid and the interruptible load total work of calling
Rate；In ADHDP algorithm, x (t) and u (t) are the inputs for evaluating network, performance index function definition corresponding with the system
Are as follows:
Wherein U (x (t), u (t), t) is utility function, is expressed as the scheduling cost of current t period, and γ is discount factor, and 0
The output of network is evaluated in≤γ≤1For approximated cost function J, realized by minimizing error below；
In formula,Wherein WcTo evaluate network parameter；As the E for all periodsc(t)=0 it when, shows
So haveTherefore error function is minimized, a trained neural network will be obtained, export the estimation for J；
1) micro-capacitance sensor lower layer Optimal Operation Model is converted
Micro-capacitance sensor scheduling includes 24 scheduling slots.In each period, made with the state-of-charge of battery t period initial time
For state variable, then micro-capacitance sensor state variable: x (t)=E (t) makes system input variable u1(t)=Pgrid(t),u2(t)=PBD
(t)-PGB(t), u3(t)=Psl(t)；
Micro-capacitance sensor scheduling model control variable is expressed as:
U (t)=(Pgrid(t),PBD(t)-PGB(t),Psl(t)) (31)
The state variable of lower layer's Optimized Operation subsystem subsequent time indicates are as follows:
X (t+1)=f (x (t), u (t), t)=x (t)-(u2(t)-PRB(t))η(u2(t)-PRB(t)) (32)
Its utility function is defined as:
U (x (t), u (t), t)=Cgrid(t)u1(t)+F(u3(t))+Cbat(x(t+1)-x(t)) (33)
In formula, CbatFor the amortization charge of energy storage device charge and discharge per unit electric energy；
Objective function is rewritten as performance index function:
In formula, μ (t)=(u (t), u (t+1) ... u (23)) represents control sequence from the t period to the 23rd period；
Optimal performance index function representation are as follows: J*(x (t), t)=min { J (x (t), μ (t), t) }；
2) iteration searching process
The training that network is first completed to evaluation network and executed before optimal cost characteristic index function, it is appropriate to obtain two networks
Hidden layer number of nodes, learning rate, discount factor and update two network weights adjusting；
The iteration cycle process of optimal cost characteristic index function includes outer loop and internal circulation, performance of the outer loop to optimization
Target function is compared, and chooses any positive definite integral form Φ (x (0), u (0)) as initial performance target function:
J0(x (0), μ (0))=Φ (x (0), u (0)) (35)
Initial control sequence indicates are as follows:
Performance index function updates are as follows:
J1(x (0), μ (0)=J (x (0), μ0(0)) (37)
Work as i=1, when 2 ..., outer loop iteration between formula (38) and formula (39):
Ji+1(x (0), μ (0)=J (x (0), μi(0)) (39)
If following formula (40) is set up, outer loop stops；
||Ji+1(x(0),μi+1(0))-Ji(x (0), μi(0))||≤ε (40)
In formula, ε is computational accuracy；
The cost function that outer loop is optimized, however control sequence cannot directly obtain, and need to carry out internal circulation and come
To control sequence；
Here define l be inner iterative number, l=0,1 ..., 23.I=0., when l=0, initial performance target function is indicated are as follows:
Work as i=0, l=0,1 ..., when 23, inside circulates between (43) the two update operations of following formula (42) and formula and continuously changes
Generation:
For any i=1,2 ..., j=0, when 1 ..., 23, inside circulates in (45) the two update operations of following formula (44) and formula
Between subsequent iteration:
Inside circulation is to obtain the control variable of each period, therefore the number of iterations is determining；Outer loop be in order to
Optimal cost value is obtained, therefore the number of iterations is uncertain；
Step 5: the superiority of the method is verified using emulation experiment.
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