CN108133394A - Consider the controllable burden demand response menu pricing method of consumer's risk preference - Google Patents
Consider the controllable burden demand response menu pricing method of consumer's risk preference Download PDFInfo
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Abstract
The present invention discloses a kind of controllable burden demand response menu pricing method for considering consumer's risk preference, by excavating different risk partiality features of the power consumer to controllable burden demand response, using the demand of controllable burden user to be oriented to, consider the Requirements Risks preference characteristics of different user and divide user type；Using demand response electricity price and load reduction as menu option, structure is with the controllable burden demand response menu pricing model of the minimum object function of system power supply totle drilling cost, and solution is optimized to model, to obtain optimal controllable burden demand response menu, selected for power consumer.The present invention pricing method can to the user of different risk partialities carry out effective district every, increase autonomous selectivity of the user to electricity price, improve user participation enthusiasm, it is easy to accomplish electric power resource is distributed rationally.
Description
Technical field
The present invention relates to can only network optimization coordinated scheduling technical field, particularly it is a kind of consider consumer's risk preference can
Control workload demand response menu pricing method.
Background technology
With the continuous social and economic development, people also increase the demand of electric power year by year, and energy resources are in short supply, the energy
The problems such as crisis and environmental pollution, is increasingly prominent.Meanwhile intermittent regenerative resource largely accesses power grid, it is steady safely to system
Fixed operation causes a series of influences of such as unbalanced power.Therefore, a large amount of controllable burden resource ginsengs of Demand-side how to be made full use of
Dispatching operation management with power grid becomes an important directions of current electric power research.
Under generated output deficiency situation, low-frequency load shedding is typically to solve the effective ways of power shortage.However, in electric power
It is traditional to be changed with rigid power cuts to limit consumption to meet the situation of system frequency requirement and the equilibrium of supply and demand under market environment.
As the important component of demand side management, Interruptable-Load Management gradually causes the attention of Utilities Electric Co..In grid generation
Undercapacity or power supply marginal cost are higher than in the case of power supply marginal benefit, and Interruptable-Load Management becomes the master of power grid peak clipping
Means are wanted, achievees the purpose that alleviate imbalance between power supply and demand, improve the level of resources utilization.In terms of power system capacity angle, it can interrupt negative
Lotus manages the spare capacity equivalent to increase system in peak load, can effectively inhibit the rapid growth of load, delay electric power
Investment reduces power supply pressure.
Important means of the demand response as demand side management reduces the direct benefit of load by issuing the user with induction property
The signal of notice or power price rising is repaid, excitation user changes it and uses power mode, reaches reduction or elapse the electricity consumption of certain period
Behavior has many advantages, such as that reflection is rapid, cost is small and environmental protection.Bridge of the demand response electricity price as contact both sides of supply and demand,
User can be guided to participate in Operation of Electric Systems and management, the effect for realize peak load shifting, optimizing allocation of resources.Therefore, it designs
Demand response Price Mechanisms effectively and reasonably, the dispatching of power netwoks that participates in for promoting user positive have great theory significance and answer
With value.
Invention content
In electric power safety, consumer's risk preference is characterized as Bearing degree of the consumer to electrical hazards.User participates in needing
When asking response, system can give the certain economic compensation of user so that user to be promoted to change the power mode of itself and is cut down centainly
Load power.However, for different making up prices, different users has different load reductions, valency even of the same race
Under lattice, due to the difference of the user properties such as consumer's risk preference, economic situation, loss or effect that the reduction of load is brought to user
With also different, therefore, different user differs the risk partiality of load reduction, what some users were born according to oneself
Risk ability is intended to cut down more loads, the load of some users cuts down then unobvious.When user receives demand response letter
Number when, the compensation electricity price that be provided according to system and itself the risk partiality characteristic of load reduction can be responded, and determine
The size of load reduction.
The technical problem to be solved in the present invention is, for a large amount of controllable burden of Demand-side, to consider consumer's risk preference
Difference, and user type is divided according to different risk partialities, it proposes to compensate electricity price and load reduction as menu using demand response
Option is combined, using system power supply cost minimization as the controllable burden demand response menu pricing method of target.
The technical solution that the present invention takes is：A kind of controllable burden demand response menu pricing for considering consumer's risk preference
Method, including：
S1 obtains power consumer and participates in the compensation electricity price of demand response and load reduction historical data；
S2 calculates the risk goal function of each power consumer participation demand response based on the data that S1 is obtained, then basis
Risk goal function classifies to user, and it is Δ P to define the corresponding customer charge reduction per a kind of risk goal functionDR,
Compensation electricity price is λ；
S3 considers electric power netting safe running constraint, and the corresponding load abatement electricity of various risks preference coefficient and demand are rung
Electricity price variable as an optimization should be compensated, using cost minimization of powering as optimizing scheduling target, builds controllable burden demand response menu
Pricing model；
S4 obtains real-time power consumer load and power plant power generation related data, the data prediction electricity based on acquisition
Vacancy；
S5, based on the electricity vacancy that S4 is calculated, to the controllable burden demand response menu pricing models of S3 structures into
Row Optimization Solution obtains corresponding each type load abatement electricity and the combination of demand response compensation electricity price during power supply cost minimum；
S6, each type load abatement electricity and the demand response that S5 is calculated compensate the combination of electricity price as controllable burden
Demand response menu option includes the controllable burden demand response signal of demand response menu to power consumer publication.
The present invention finally the load reduction into the menu that user exports comprising corresponding inhomogeneity risk goal function and
Compensate the combination of electricity price.User can select suitable menu option with the load abatement amount that can actually bear according to actual needs,
Since the present invention has been contemplated that user demand responds risk goal function in menu pricing, in practical demand response
When, the actual selection of user is often corresponding to its own risk factor, that is, has ensured controllable burden demand response band list price mould
The realization that the final power supply cost of type minimizes, while the system made, in electricity peak period, can dispatch more Demand-sides can
Burdened resource is controlled, is avoided using excessive conventional thermal power generation resource.
Preferably, in the present invention, user participates in the risk goal function of demand response and customer charge reduction is Δ PDR
And compensation electricity price be λ between correlativity be：I.e. user participate in demand response characteristic be：The risk of user
Preference coefficient is bigger, and the load that can cut down is more, and desired one timing of load abatement amount, and electricity price is higher, and consumer's risk is inclined
Good coefficient will also increase.The calculation formula of correlativity formula and non-final preference coefficient, the present invention are utilized according to historical data
Available data digging technology combination user demand statistics determines risk goal function.
The present invention, can be different according to K be calculated after each power consumer demand response preference coefficient is calculated
As a result directly divide K classes, per an a kind of i.e. corresponding coefficient θk.Preferably, the demand of each power consumer being calculated is rung
Risk goal function is answered, defines and corresponds to a demand response preference coefficient section respectively per a kind of risk goal function, take each
The median in section is as such corresponding demand response preference coefficient θk。
Preferably, in S3, the optimized variable of controllable burden demand response menu pricing model further includes each fired power generating unit
Output Pg, Optimized Operation object function is：
Wherein, FGFor fired power generating unit production cost, FCFired power generating unit Environmental costs, FDRBenefit for controllable burden demand response
Repay cost；Pgi、λkWith Δ PDR, kFor Optimal Decision-making variable；
Fired power generating unit production cost FGFor：
N in formulagFor the number of fired power generating unit in system, PgiFor the output of fired power generating unit i, ai、biAnd ciRespectively thermal motor
Secondary, primary, the constant term cost coefficient of group i；
Thermoelectricity Environmental costs FCFor：
In formula,βi、γi、ζiWithIt is the emission performance coefficient of fired power generating unit i, CenvFor thermoelectricity Environmental costs coefficient；
The cost of compensation F of controllable burden demand responseDRFor：
Δ P in formulaDR,kBelong to the customer charge reduction of kth class, υ for risk goal functionkIt is kth class user in whole
Accounting in power consumer, λkCompensation electricity price for corresponding kth class customer charge reduction.It can be seen that the present invention is optimizing tune
When spending, it is contemplated that energy conservation and environmental protection factor, by the part having an effect as optimization of fired power generating unit.
Preferably, in S3, the electric power netting safe running constraint includes：
S31 system balancings constrain：
In formulaFor system fired power generating unit gross capability,For system total load, ∑ Δ PDRIt is used for system controllable burden
The total load reduction in family, network power losses are ignored in system；
S32 fired power generating unit units limits：
Pgi min≤Pgi≤Pgi max
P in formulagi minAnd Pgi maxThe minimum and maximum of respectively fired power generating unit i is contributed；
S33 power network securities constrain：
L=1 in formula, 2 ..., L represent branch, and L represents branch sum, ηilRepresent the injecting power of branch l to node i
Sensitivity coefficient, N be system total node number, Pl maxRepresent the maximum transmission power of circuit；
S34 controllable burden power extractions amount constrains：
0≤ΔPDR,k≤Pdθk
P in formuladWorkload demand amount during demand response, θ are not involved in for controllable burden userkRisk for kth class user is inclined
Good coefficient.
In step S4 of the present invention, the prior art is predicted as to future time electricity vacancy.
Preferably, S5 of the present invention uses multi-objective particle swarm MOPSO (Multi-objective particle swarm
Optimization algorithm) algorithm solution controllable burden demand response menu pricing model.
Advantageous effect
Compared with prior art, the present invention has the following advantages and improves：
(1) present invention considers influence of the different risk goal functions to menu pricing, for controllable burden user, with wind
The increase of dangerous preference coefficient, system can correspondingly improve demand response electricity price and promote to cut down more load powers, obtain user
More economic compensation are obtained, so as to improve the enthusiasm of user's participation.Although system pay user demand response compensation into
This increases with the increase of Requirements Risks preference coefficient, but due to reducing the use of thermal power generation resource, system it is total
Power supply cost will more reduce.Therefore, it is necessary to consider different risk partiality types user demand response carry out menu pricing with
Promote user's active response, save system power supply cost；
(2) consider influence of the menu pricing of different risk partialities to system power supply cost and cost of compensation in the present invention,
Although the menu pricing for considering user's difference risk partiality increases the demand response cost of compensation that system pays user, but
It is the customer charge reduction bigger due under the pattern of menu pricing, makes system that can dispatch more in electricity peak period
Demand-side controllable burden resource, avoid using excessive conventional thermal power generation resource, reduce system thermal power generation cost and
Thermoelectricity running environment cost, so as to reduce the total power supply cost of system.Accordingly, it is considered to different risk partiality demand response menus are determined
Valency can efficiently reduce system power supply cost.
Description of the drawings
Fig. 1 is the method flow schematic diagram of the present invention；
Fig. 2 is a kind of flow diagram of specific embodiment of the present invention；
Fig. 3 is controllable burden demand response menu pricing time diagram of the present invention；
Fig. 4 is the menu pricing influence factor relation schematic diagram of the present invention；
Fig. 5 is a kind of electric system single line schematic diagram of the IEEE-30 nodes of specific embodiment of the present invention；
Fig. 6 is multi-objective particle swarm algorithm flow diagram.
Specific embodiment
It is further described below in conjunction with the drawings and specific embodiments.
Refering to what is shown in Fig. 1, the present invention considers the controllable burden demand response menu pricing method of consumer's risk preference, packet
It includes：
S1 obtains power consumer and participates in the compensation electricity price of demand response and load reduction historical data；
S2 calculates the risk goal function of each power consumer participation demand response based on the data that S1 is obtained, then basis
Risk goal function classifies to user, and it is Δ P to define the corresponding customer charge reduction per a kind of risk goal functionDR,
Compensation electricity price is λ；
S3 considers electric power netting safe running constraint, and the corresponding load abatement electricity of various risks preference coefficient and demand are rung
Electricity price variable as an optimization should be compensated, using cost minimization of powering as optimizing scheduling target, builds controllable burden demand response menu
Pricing model；
S4 obtains real-time power consumer load and power plant power generation related data, the data prediction electricity based on acquisition
Vacancy；
S5, based on the electricity vacancy that S4 is calculated, to the controllable burden demand response menu pricing models of S3 structures into
Row Optimization Solution obtains corresponding each type load abatement electricity and the combination of demand response compensation electricity price during power supply cost minimum；
S6, each type load abatement electricity and the demand response that S5 is calculated compensate the combination of electricity price as controllable burden
Demand response menu option includes the controllable burden demand response signal of demand response menu to power consumer publication.
The present invention finally the load reduction into the menu that user exports comprising corresponding inhomogeneity risk goal function and
Compensate the combination of electricity price.User can select suitable menu option with the load abatement amount that can actually bear according to actual needs,
Since the present invention has been contemplated that user demand responds risk goal function in menu pricing, in practical demand response
When, the actual selection of user is often corresponding to its own risk factor, that is, has ensured controllable burden demand response band list price mould
The realization that the final power supply cost of type minimizes, while the system made, in electricity peak period, can dispatch more Demand-sides can
Burdened resource is controlled, is avoided using excessive conventional thermal power generation resource.
In the present invention, user participates in the risk goal function of demand response and customer charge reduction is Δ PDRWith compensation electricity
Correlativity between valency is λ is：I.e. user participate in demand response characteristic be：The risk goal function of user
Bigger, the load that can cut down is more, and desired one timing of load abatement amount, and electricity price is higher, consumer's risk preference coefficient
It will increase.The calculation formula of correlativity formula and non-final preference coefficient, the present invention calculate user demand according to historical data
It responds risk goal function and utilizes available data digging technology.
The present invention, can be different according to K be calculated after each power consumer demand response preference coefficient is calculated
As a result directly divide K classes, per an a kind of i.e. corresponding coefficient θk.Preferably, the demand of each power consumer being calculated is rung
Risk goal function is answered, defines and corresponds to a demand response preference coefficient section respectively per a kind of risk goal function, take each
The median in section is as such corresponding demand response preference coefficient θk。
In step S4 of the present invention, the prior art is predicted as to future time electricity vacancy.
S5 of the present invention uses multi-objective particle swarm MOPSO (Multi-objective particle swarm
Optimization algorithm) algorithm solution controllable burden demand response menu pricing model.
Embodiment 1
In the present embodiment, the present invention is in use, be as follows：
Step 1, power scheduling person is according to the data of load in Real-time markets and power plant, the charge condition of forecasting system,
Obtain electricity vacancy amount；
In Spot electricity market, when power scheduling person predicts that a certain moment electrical demand changes greatly, electricity supply and demand is not
Balance, electric power system dispatching person, which will shift to an earlier date to user, issues demand response signal, and by price incentive, user increases or decreases
Load power is to meet Real-time Balancing；
Step 2, for electricity vacancy situation, power scheduling person issues demand response signal to society；
Electric power can bring itself risk tolerance according to electricity price and power breakdown, have as a kind of commodity, user
Decide whether to participate in dispatching of power netwoks in the time of limit.Since the risk partiality characteristic of user is different, the degree of user demand response
That is load power incrementss or the size of reduction also differ；
Step 3, user participates in the demand response of power scheduling person's publication, cuts down load power；
When user participates in dispatching of power netwoks, the degree of demand response, the i.e. need of the size of load power reduction and user
Risk partiality characteristic is asked to be positively correlated relationship, relationship is negatively correlated with compensation electricity price.Effectively to reflect user to load reduction
Preference, present invention introduces Requirements Risks preference coefficient θ, and choose common linear function form to describe controllable burden
Relationship between the load reduction of demand response, making up price and Requirements Risks preference coefficient：
Δ P in formulaDRLoad power reduction for controllable burden user；λ cuts down compensation electricity price for load power；θ is can
Load user demand risk goal function is controlled, characterizes user due to the difference of Risk Tolerance and to load power reduction
Preference size.Formula is controllable burden demand response function, reflects customer charge reduction with making up price and consumer's risk
The difference of preference coefficient and it is different.
Step 4, power scheduling person collects the information of each side in market and carries out market clearing with system power supply cost minimization,
If system power supply cost is not minimum, repeatedly step 4, if system power supply cost is minimum, step 5 is jumped to；
For a large amount of controllable burden of Demand-side, the difference of its risk partiality is considered, and divide according to different risk partialities
User type, propose to compensate electricity price and load reduction as menu combination option using demand response, with system power supply cost most
The small controllable burden demand response menu pricing method for target.
1) according to the size of consumer's risk preference coefficient, user is divided into 1,2 ..., type in K, each grade of user's
Risk goal function is respectively θ1, θ2..., θK(θ1＜ θ2＜ ... ＜ θK), the corresponding user's accounting of type is υ1,
υ2..., υK, and meet υi＞ 0 and υ1+υ2+…+υK=1.
2) selection demand response compensation electricity price λ and load reduction Δ PDRAs 2 " menu " projects.Consider that user is different
Risk partiality, respectively controllable burden user formulates K kinds " menu option ", the corresponding menu electricity price of each option and negative
Lotus reduction is (λ1, Δ PDR, 1)、(λ2, Δ PDR, 2) ..., (λK, Δ PDR, K), detailed menu parameter explanation is as shown in table 1.
1 menu parameter of table
| Menu shelf grade | Risk goal function | Combine electricity | Combine electricity price | User's accounting |
| 1 | θ1 | ΔPDR, 1 | λ1 | υ1 |
| 2 | θ2 | ΔPDR, 2 | λ2 | υ2 |
| … | … | … | … | … |
| K | θK | ΔPDR, K | λK | υK |
Therefore, according to the user type of division, type θ is belonged to for risk partialityiThe demand response letter of controllable burden
Number can be expressed as：
In formula, Δ PDR, iBelong to θ for risk partialityiThe power extraction amount of the controllable burden user of type；λiFor θiType can
It controls load power and cuts down compensation electricity price.
3) power scheduling person considers network security factor and energy-saving ring according to the data of load in Real-time markets and power plant
Under the premise of guarantor, with the minimum target of the total power supply cost of system, market clearing is carried out, determines menu electricity price option, is i.e. compensation electricity
Valency and load reduction.Including building controllable burden demand response menu pricing model：
The optimization object function of controllable burden demand response menu pricing model：
In electricity market, price main body power scheduling person is considering energy conservation and environmental protection, is meeting power network security operation about
Under the premise of beam etc., with the minimum regulation goal structure controllable burden demand response menu pricing model of the power supply cost of system.
F in formulaG, FC, FDRFor 3 parts of system power supply cost, respectively fired power generating unit production cost, fired power generating unit ring
Border cost, system pay the cost of compensation of controllable burden demand response；Pgi, λk, Δ PDR, k, it is Optimal Decision-making variable.Fire
The expression formula of motor group production cost is
N in formulagNumber for fired power generating unit in system；PgiOutput for fired power generating unit i；ai, bi, ciRespectively thermal motor
Secondary, primary, the constant term cost coefficient of group i.
Thermoelectricity Environmental costs are embodied in fired power generating unit in the process of running to the exhaust gas of environmental emission, thus thermoelectricity environment into
Originally it can be described with the emission performance of thermal power generation unit, i.e.,
In formula,βi, γi, ζi,It is the emission performance coefficient of fired power generating unit i, can be obtained by measuring；CenvFor
Thermoelectricity Environmental costs coefficient.
The cost of compensation of controllable burden demand response is mainly reflected in system and pays the customer charge for participating in demand response
The reimbursement for expenses of reduction, value are making up price and load reduction and the product of the number of users accounting of the type, mathematics
Expression formula is
Δ P in formulaDR,kBelong to θ for risk goal functionkThe load reduction of type of user.
Constraints
A system balancings constrain (ignoring system power dissipation).
In formulaFor system fired power generating unit gross capability；For system total load；∑ΔPDRIt is used for system controllable burden
The total load reduction in family.
B fired power generating unit units limits.
Pgi min≤Pgi≤Pgi max
P in formulagi min, Pgi maxThe respectively minimum of fired power generating unit i, maximum output.
C power network securities constrain.
L=1 in formula, 2 ..., L represent branch, and L represents branch sum；ηilRepresent the injecting power of branch l to node i
Sensitivity coefficient；Pl maxRepresent the maximum transmission power of circuit.
D controllable burden power extractions amount constrains.
0≤ΔPDR,k≤Pdθk
P in formuladWorkload demand amount during demand response, the maximum value that load power is cut down are not involved in for controllable burden user
It is related with the type of preferences belonging to user.
Step 5, power scheduling person issues scheduling result, determines optimal menu electricity price option, that is, compensates electricity price and load is cut
Decrement.
Several main bodys or service are combined sale by the menu pricing main body that refers to fix a price, and according to consumption figure number or
The height of quality combines each product (service) and carries out price discrimination, is suitble to its wind to realize to provide to different user groups
The combinations of services of dangerous preference.The design of menu electricity price includes the content of 2 aspects：The combination of selection business and determining for combinations of services
Valency.In controllable burden demand response menu pricing, the formation of menu electricity price is by user, power scheduling person, 3, market aspect
Collective effect, relationship are as shown in Figure 4.
Power scheduling person can issue demand response signal according to the system charge situation of prediction first, and user rings for demand
Induction signal responds and reduction plans, power scheduling person collect in market the information of each side and with system power supply cost minimization into
Row market clearing when system power supply cost minimization and when no longer changing, stops demand response plan, exports optimal menu option
Combination is as shown in table 2.
2 menu option of table combines
Embodiment 2
The present embodiment for the IEEE-30 nodes of simulation calculation electric system line chart as shown in fig. 5, it is assumed that 1,2,
5th, 8,11 nodes are respectively connected to conventional thermal power unit, access controllable burden user in node 3, controllable burden general power is 500MW.
The coefficient of electrical power generators cost function is as shown in table 3, and the coefficient of generator discharge flow function is as shown in table 4.
The coefficient of 3 electrical power generators cost function of table
4 generator of table discharges the coefficient of flow function
Step S1 and S2, the historical quotes that grid company participates in demand response according to marketing users carry out data analysis, choosing
Take in June, 2015 every night 17: 30 when 600 groups of users quote data carry out linear fit, and cut according to load in quotation information
Consumer's risk preference is divided into 4 grades, that is, takes K=4, θ is obtained by the size of decrement1=0.14, θ2=0.25, θ3=0.32, θ4
=0.56, and the number accounting of counting user is respectively υ1=0.35, υ2=0.30, υ3=0.26, υ4=0.19.
Step S3, price main body power scheduling person is before considering energy conservation and environmental protection, meeting power network security operation constraint etc.
It puts, with the minimum regulation goal structure controllable burden demand response menu pricing model of the power supply cost of system.
Step S4, power scheduling person is according to the data of load in Real-time markets and power plant, the charge condition of forecasting system,
Obtain electricity vacancy amount.
Step S5, power scheduling person are collected the information of each side in market and are gone out with system power supply cost minimization progress market
Clearly, computing system operating cost minimum value and the optimal solution of controllable burden demand response menu pricing model.
The optimization algorithm that the present invention solves controllable burden demand response menu pricing model uses particle cluster algorithm.
The crucial local optimum and global optimum for seeking to determine particle in particle cluster algorithm.Particle by the experience of itself and
Best experience updates the movement of next step in group.Its renewal process is：
Vi+1=w × Vi+c1×rand()×(pbesti-Xi)+c2×rand()×(gbesti-Xi)
Xi+1=Xi+Vi+1
Wherein, ViIt is the speed of particle；XiIt is the position of particle；It is best that pbest is up to the present each particle is found
Position；Gbest is the desired positions that all particles are found in entire group；Rand () is the random number between (0,1)；XiIt is grain
The current location of son；c1And c2It is Studying factors；W is the changeable weight value of population, and value is：
Wherein, wmaxFor initial inertia weight；wminInertia Weight during iteration to maximum iteration；intermaxFor maximum
Iterations；Inter is current iteration number.
The present invention utilizes multi-objective particle swarm MOPSO (Multi-objective particle swarm
Optimization algorithm) algorithm solution controllable burden demand response menu pricing model.The algorithm is to be based on
Pareto layer sorting principles select individual, before can obtaining excellent Pareto when solving multi-objective optimization question
Edge.The Optimizing Flow of multiple target controllable burden demand response menu pricing is as shown in Figure 6.
Multiobjective Optimal Operation concrete operations flow is as follows：
1) data initialization.Input system topological structure parameter, model parameter, MOPSO algorithm parameters etc..Meanwhile initially
Change particle populations, each particle individual corresponds to compensation electricity price in a demand response period in population and load is cut down
Measure assembled scheme.
2) simulation model is inputted using particle individual as system variable, the variable for violating constraint is modified, and calculates
Fired power generating unit production cost, fired power generating unit Environmental costs, system are paid the cost of compensation of controllable burden demand response and are punished
Item is penalized as ideal adaptation angle value.
3) by the input of individual adaptation degree model as an optimization, progeny population is obtained by particle more new formula.
4) individual extreme value pbest is determined.Using pbest as the initial individuals extreme value of particle, if current particle dominates
Pbest, then using current particle as pbest individual extreme values；If the two cannot compare, calculate the two and dominated in group
The number of other particles is dominated more at most as individual extreme value pbest.
5) layer sorting is carried out to population, optimal non-domination solution Pareto is stored in external archival set, is removed non-
Pareto is solved, and judges whether external archival set is more than specified volume, if so, choosing m particle according to crowding distance.
6) global optimum gbest.The Pareto optimal solutions preserved using external archival set, herein cited roulette side
Method chooses gbest in gathering according to the crowding distance of optimal solution from outside.
7) small probability makes a variation.To prevent that MOPSO algorithms from prematurely converging to local optimum forward position and not before global optimum
Edge, introduces small probability random variation mechanism herein, and the small probability for generating ± 30% on original position to the position of particle is disturbed
It is dynamic, increase the optimizing ability in particle global optimum forward position.
8) return to step 3), until meeting end condition.End condition is taken to be exported final for maximum iteration herein
Optimum results.
The present invention is programmed using matlab7.6, and the relevant parameter value of MOPSO is：Particle population size is 20, and maximum changes
Generation number is 200, c1Take 1.2, c2Take 1.2, wmaxIt is taken as 0.9, wmin0.4 is taken as, external archival set size is 80, and variation is general
Rate is 10%.
Step S6, power scheduling person determine optimal menu electricity price option, that is, compensate all kinds of groups of electricity price and load reduction
It closes, power consumer is distributed to by demand response signal.
Consider that the controllable burden demand response menu power price grouped of different risk partialities is as shown in table 5, it can be seen that examining
Under the menu pricing pattern for considering user's difference risk partiality, respectively for 4 kinds of different risk partiality types design 4 grades it is different
Menu electricity price options for user selects, and so as to improve the autonomous selectivity of user, promotes different risk partiality type of user actively
The enthusiasm of participation.In practical electricity market, user can select corresponding menu electricity according to the risk partiality type of itself
Valency option participates in dispatching of power netwoks.With the raising of risk goal function, the load reduction in menu option also increase therewith it is small, but
It is that compensation electricity price also accordingly increases, to promote user that such menu is selected to combine, cuts down more load powers.
5 demand response menu electricity price option of table
The above, the only specific embodiment in the present invention, but protection scope of the present invention is not limited thereto are appointed
What be familiar with the people of the technology disclosed herein technical scope in, it will be appreciated that the transformation or replacement expected should all be covered
Within the scope of the present invention, therefore, protection scope of the present invention should be subject to the protection domain of claims.
Claims (6)
1. a kind of controllable burden demand response menu pricing method for considering consumer's risk preference, it is characterized in that, including：
S1 obtains power consumer and participates in the compensation electricity price of demand response and load reduction historical data；
S2 calculates the risk goal function of each power consumer participation demand response based on the data that S1 is obtained, then according to risk
Preference coefficient classifies to user, and it is Δ P to define the corresponding customer charge reduction per a kind of risk goal functionDR, compensation
Electricity price is λ；
S3 considers electric power netting safe running constraint, and the corresponding load abatement electricity of various risks preference coefficient and demand response are mended
Electricity price variable as an optimization is repaid, using cost minimization of powering as optimizing scheduling target, builds controllable burden demand response menu pricing
Model；
S4 obtains real-time power consumer load and power plant power generation related data, the data prediction electricity vacancy based on acquisition；
S5 based on the electricity vacancy that S4 is calculated, carries out the controllable burden demand response menu pricing model of S3 structures excellent
Change and solve, obtain corresponding each type load abatement electricity and the combination of demand response compensation electricity price during power supply cost minimum；
S6, each type load abatement electricity and the demand response that S5 is calculated compensate the combination of electricity price as controllable burden demand
Menu option is responded, the controllable burden demand response signal of demand response menu is included to power consumer publication.
2. according to the method described in claim 1, it is characterized in that, user participate in demand response risk goal function born with user
Lotus reduction is Δ PDRAnd compensation electricity price be λ between correlativity be：
3. according to the method described in claim 2, it is characterized in that, for the demand response risk for each power consumer being calculated
Preference coefficient defines and corresponds to a demand response preference coefficient section respectively per a kind of risk goal function, takes each section
Median is as such corresponding demand response preference coefficient θk。
4. according to the method described in claim 1, it is characterized in that, in S3, controllable burden demand response menu pricing model it is excellent
Change the output P that variable further includes each fired power generating unitg, Optimized Operation object function is：
Wherein, FGFor fired power generating unit production cost, FCFired power generating unit Environmental costs, FDRFor controllable burden demand response compensation into
This；Pgi、λkWith Δ PDR, kFor Optimal Decision-making variable；
Fired power generating unit production cost FGFor：
N in formulagFor the number of fired power generating unit in system, PgiFor the output of fired power generating unit i, ai、biAnd ciRespectively fired power generating unit i
Secondary, primary, constant term cost coefficient；
Thermoelectricity Environmental costs FCFor：
In formula,βi、γi、ζiWithIt is the emission performance coefficient of fired power generating unit i, CenvFor thermoelectricity Environmental costs coefficient；
The cost of compensation F of controllable burden demand responseDRFor：
Δ P in formulaDR,kBelong to the customer charge reduction of kth class, υ for risk goal functionkIt is kth class user in whole electric power
Accounting in user, λkCompensation electricity price for corresponding kth class customer charge reduction.
5. according to the method described in claim 4, it is characterized in that, in S3, electric power netting safe running constraint includes：
S31 system balancings constrain：
In formulaFor system fired power generating unit gross capability,For system total load, NdFor the number of load bus in system, ∑
ΔPDRThe load reduction total for system controllable burden user；
S32 fired power generating unit units limits：
Pgi min≤Pgi≤Pgi max
P in formulagi minAnd Pgi maxThe minimum and maximum of respectively fired power generating unit i is contributed；
S33 power network securities constrain：
L=1 in formula, 2 ..., L represent branch, and L represents branch sum, ηilRepresent injecting power from branch l to node i it is sensitive
Spend coefficient；Pl maxRepresent the maximum transmission power of circuit；
S34 controllable burden power extractions amount constrains：
0≤ΔPDR,k≤Pdθk
P in formuladWorkload demand amount during demand response, θ are not involved in for controllable burden userkRisk partiality system for kth class user
Number.
6. according to the method described in claim 1, it is characterized in that, S5 is solved controllable negative using multi-objective particle swarm MOPSO algorithms
Lotus demand response menu pricing model.
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