CN109685197A - A kind of optimization method based on the power supply set meal for improving particle swarm algorithm - Google Patents
A kind of optimization method based on the power supply set meal for improving particle swarm algorithm Download PDFInfo
- Publication number
- CN109685197A CN109685197A CN201811621364.6A CN201811621364A CN109685197A CN 109685197 A CN109685197 A CN 109685197A CN 201811621364 A CN201811621364 A CN 201811621364A CN 109685197 A CN109685197 A CN 109685197A
- Authority
- CN
- China
- Prior art keywords
- user
- power supply
- set meal
- price
- supply set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000012054 meals Nutrition 0.000 title claims abstract description 164
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 30
- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 239000002245 particle Substances 0.000 title claims abstract description 23
- 230000005611 electricity Effects 0.000 claims abstract description 90
- 230000006870 function Effects 0.000 claims abstract description 67
- 239000011159 matrix material Substances 0.000 claims abstract description 37
- 230000008901 benefit Effects 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000035945 sensitivity Effects 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 6
- 239000000203 mixture Substances 0.000 claims description 6
- 230000008929 regeneration Effects 0.000 claims description 4
- 238000011069 regeneration method Methods 0.000 claims description 4
- 230000017105 transposition Effects 0.000 claims description 4
- 241000208340 Araliaceae Species 0.000 claims description 3
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims description 3
- 235000003140 Panax quinquefolius Nutrition 0.000 claims description 3
- 235000008434 ginseng Nutrition 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 2
- 238000013139 quantization Methods 0.000 abstract description 4
- 230000006399 behavior Effects 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 239000000047 product Substances 0.000 description 5
- 238000012546 transfer Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008447 perception Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000019637 foraging behavior Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000002715 modification method Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Computational Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Game Theory and Decision Science (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
This application involves a kind of optimization methods based on the power supply set meal for improving particle swarm algorithm, this method comprises: the factor of influence user's viscosity under step 1, analysis Power Market, using the price for the set meal that will power as optimization object, wherein factor includes the price of power supply set meal；Step 2, using sticky function, user's viscosity of single user is quantified, and according to multiple sticky functions, determine the decision matrix of user group；Step 3, according to sticky function and decision matrix, establish the total utility function of reaction sale of electricity quotient comprehensive benefit；Step 4, using predefined assimilatory coefficient, the price of quantization power supply set meal is to the influence degree of the decision of different user, and according to assimilatory coefficient, establishes the decision model of user；Step 5, according to total utility function, decision model and improve particle swarm algorithm optimizing, determine optimal package price, thus sale of electricity quotient can provide with competition in retail set meal, and then promoted company's benefit.
Description
Technical field
This application involves technical field of electric power more particularly to a kind of optimizations based on the power supply set meal for improving particle swarm algorithm
Method.
Background technique
With the deep and expansion of China's sales market degree of opening and range, autonomous choosing of the power consumer to sale of electricity quotient
The power of selecting will be decontroled gradually.Sale of electricity quotient tends to menu electricity price, listed power price and set meal electricity price (or price of power supply set meal)
Equal considerations are sold the electricity pricing mode of selection.
At retail market open initial stage, sale of electricity quotient's negligible amounts reasonably adjust management strategy according to turn of the market, optimize
The price of power supply set meal, expanding income on it has very important influence.Currently, power retailing set meal is formulated both at home and abroad
Study relatively fewer, the domestic research in terms of the formulation and optimization of set meal (or power supply set meal) is mainly telecom charges set
Meal, mostly establishes objective function to the influence degree of product rate according to factors such as cost, demand and customer perceptions, selects
Suitable algorithm is selected in the case where meeting constraint condition, searches for corresponding product rate under optimal objective function.
In addition, particle swarm optimization algorithm (Particle Swarm Optimization, abbreviation PSO) is to open in a kind of modern times
Hairdo algorithm, it is that the simple birds foraging behavior of simulation proposes, and the optimization algorithm phase based on group behavior of early stage
Have greater advantage in calculating speed and consumption memory than, PSO algorithm because its realization only need simple mathematical operation and compared with
Few program code.Compared with most evolution algorithms, the advantage of PSO algorithm is by " cooperation " between individual rather than " suitable person is raw
Deposit " search optimal solution, each of group individual has memory to the path before oneself, refer to history valence in conjunction with consumer
The characteristics of consumption decision of lattice, so selection particle swarm algorithm carries out subregional best price search to entire sale of electricity set meal.
Summary of the invention
For market under power retailing side relieving race condition to the needs of retail power supply set meal, this application provides one kind
Optimization method based on the power supply set meal for improving particle swarm algorithm.
In view of this, this application provides a kind of optimization method based on the power supply set meal for improving particle swarm algorithm, it is described
Method the following steps are included:
The factor of influence user's viscosity under step 1, analysis Power Market, the price for the set meal that will power is as excellent
Change object, wherein the factor includes the price of the power supply set meal；
Step 2, using sticky function, user's viscosity of single user is quantified, and according to multiple sticky functions,
Determine the decision matrix of user group；
Step 3, according to the sticky function and the decision matrix, establish the total utility letter of reaction sale of electricity quotient comprehensive benefit
Number；
Step 4, using predefined assimilatory coefficient, quantify the price of the power supply set meal to the shadow of the decision of different user
The degree of sound, and according to the assimilatory coefficient, establish the decision model of user；
Step 5, according to the total utility function, the decision model and the improvement particle swarm algorithm optimizing, determine most
Excellent package price.
Optionally, in the step 2, the viscosity function representation are as follows:
In formula, N is the quantity for all users that sale of electricity quotient manages in region；N is the power supply set meal of sale of electricity quotient publication
Number；SiIndicate i-th of user (or user i) to the user of sale of electricity quotient viscosity, wherein 0 < i≤N, and i be positive it is whole
Number；kpiIndicate a n dimension group being made of element 0 or 1；kpijIndicate user i to j-th power supply set meal (or set meal of powering
J) selection situation, kpij=1 indicates decision certainly, kpij=0 indicates negative decision；The transposition of T' expression T, wherein T=[T1,
T2,...,Tn], TiIndicate the contract time limit of i-th of power supply set meal.
Optionally, in the step 2, the decision matrix is indicated are as follows:
In formula, Kp indicates the 0-1 matrix of N × n scale.
Optionally, in the step 3, the total utility function representation are as follows:
In formula, NCIndicate that the sale of electricity quotient manages actual user's quantity in region；It is sold described in expression
The average market influence power of electric business.
Optionally, in the step 4, the assimilatory coefficient is indicated are as follows:
In formula, γiIndicate the assimilatory coefficient of i-th of user；QeIndicate the user to the total quantity consumed of electricity commodity；QA
Indicate the total quantity consumed of the domestic energy of the user；υiIndicate the income increase rate of the user i；ηeIndicate the electric power quotient
The Consumer Prices index of product.
Optionally, in the step S4, the decision model is indicated are as follows:
In formula, kpij=1 indicates decision certainly, kpij=0 indicates negative decision；
Constraint condition:
Certainly the constraint condition of decision indicates are as follows:
In formula, QiFor the year electricity consumption of user i；PunitjFor the energy unit rate for the set meal j that powers；PfixedjFor the confession
The day of electric set meal j counts constant expense price；IRPitIndicate the user i obtained using IRP model in the heart reference price of t moment
Lattice；aijIt is the user i to the preference profile of the power supply set meal j, aij=0 indicates negative preference；
It negate that the constraint condition of decision indicates are as follows:
In formula, aij=1 indicates preference certainly.
Optionally, the IRP model is expressed as:
IRPit=βiP(t-1)+(1-βi)IRPi(t-1)
In formula, IRPitFor user i t moment heart reference price；IRP(t-1)Indicate all users t moment it
The average value of preceding heart reference price；βiIndicate the coefficient of sensitivity of price of the user i；P(t-1)Market before expression t moment
Average reference price.
Optionally, the step 5 includes:
Step 51, price, customer parameter and the preference coefficient of each power supply set meal are initialized, wherein institute
State the year electricity consumption and coefficient of sensitivity of price that customer parameter includes user；
Step 52, using the price and IRP model of the power supply set meal, the heart reference price of the user is calculated；
Step 53, using the calculation formula of the assimilatory coefficient, the assimilation system that custom power is consumed under current environment is calculated
Number；
Step 54, after obtaining the assimilatory coefficient of user's heart reference price and the current environment, using described
Assimilatory coefficient and the decision model determine that the user to the affirmative decision or negative decision of each power supply set meal, finally obtains
To the decision matrix of the user group；
Step 55, determine that each user affirms in the user there are in the case where preference from meeting with the presence or absence of preference
The power supply set meal of user preference is chosen in multiple power supply set meals of decision, if it does not exist, from the multiple power supplies for meeting decision certainly
Optimal power supply set meal is chosen in set meal；
Step 56, using the calculation formula of the total utility function, the value of the corresponding fitness function of each individual is calculated,
In, value of the value of the total utility function as the fitness function；
Step 57, the optimal and individual history of Population Regeneration is optimal, and population is merged into cistern of chiasma and executes crossover operation,
In, price corresponding to the value of maximum target function in the combination of the optimal price for power supply set meals all in an iteration of population
Combination, price corresponding to the value of maximum target function after the optimal current iteration power supply package price update for cut-off of individual history
Combination；
Step 58, if meeting stop condition, search stops, and exports result；Otherwise the step 52 is returned to continue searching.
Optionally, choosing optimal power supply set meal from the multiple power supply set meals for meeting affirmative decision includes:
Calculate the average price of every degree electricity of all power supply set meals for meeting decision certainly；
Select the smallest power supply set meal of average price of every degree electricity as optimal power supply set meal.
Optionally, the calculation formula of the average price indicates are as follows:
PRij=(Qi×Punitj+365×Pfixedj)/Qi
In formula, PRijIndicate average price of the user i to the every degree electricity for meeting the power supply set meal j with reference to decision certainly.
Above-mentioned technical proposal provided by the embodiments of the present application has the advantages that compared with prior art
(1), this method provided by the embodiments of the present application can provide valence for the design of the retail power supply set meal of sale of electricity quotient
Lattice reference user or possesses the power consumer of autonomous right to choose in face of guaranteeing the minimum under conditions of power retailing side is decontroled and competed,
Sale of electricity quotient can be provided with the retail set meal in competition, and then promote company's benefit.
(2), this method provided by the embodiments of the present application is also suitble to the power supply set meal of oneself to provide for power consumer selection
Price reference.In view of the different difference of the price sensitivity and electricity consumption of user, the IRP model of user is proposed, in addition, with
The reference price for reaction user's otherness that family can be obtained according to this model, to select the electricity for being suitble to oneself consumption habit
Power is sold set meal.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, for those of ordinary skill in the art
Speech, without any creative labor, is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of stream of optimization method based on the power supply set meal for improving particle swarm algorithm provided by the embodiments of the present application
Journey schematic diagram；
Fig. 2 is the schematic diagram of the model of influencing factors of user's viscosity in a kind of electricity market provided by the embodiments of the present application；
Fig. 3 is a kind of tool of optimization method based on the power supply set meal for improving particle swarm algorithm provided by the embodiments of the present application
Body flow diagram.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the application, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, shall fall in the protection scope of this application.
The embodiment of the present application provides a kind of optimization method based on the power supply set meal for improving particle swarm algorithm, such as Fig. 1 institute
Show, this method may comprise steps of: the factor of influence user's viscosity under step 1, analysis Power Market, it will be for
The price of electric set meal is as optimization object, wherein factor includes the price of power supply set meal；Step 2, using sticky function, to list
User's viscosity of a user quantifies, and according to multiple sticky functions, determines the decision matrix of user group；Step 3, basis
Sticky function and decision matrix establish the total utility function of reaction sale of electricity quotient comprehensive benefit；Step 4 utilizes predefined assimilation
Coefficient, the price of quantization power supply set meal is to the influence degree of the decision of different user, and according to assimilatory coefficient, establishes user's
Decision model；Step 5, according to total utility function, decision model and improve particle swarm algorithm optimizing, determine optimal package price.
This method provided by the embodiments of the present application can provide price ginseng for the design of the retail power supply set meal of sale of electricity quotient
It examines, under conditions of power retailing side is decontroled and competed, user or possesses the power consumer of autonomous right to choose, sale of electricity in face of guaranteeing the minimum
Quotient can be provided with the retail set meal in competition, and then promote company's benefit.
In addition, it is also suitble to the power supply set meal of oneself to provide price reference for power consumer selection.In view of the valence of user
Lattice susceptibility and the different difference of electricity consumption, propose the IRP model of user, in addition, what user can obtain according to this model
The reference price for reacting user's otherness, to select the power retailing set meal for being suitble to oneself consumption habit.
Technical solution to facilitate the understanding of the present invention to carry out above-mentioned technical proposal below by specific embodiment
Description.
A kind of optimization method based on the power supply set meal for improving particle swarm algorithm, method includes the following steps:
The factor of influence user's viscosity under step 1, analysis Power Market, the price for the set meal that will power is as excellent
Change object, wherein factor includes the price of power supply set meal；
In this step, from the particularity of electricity commodity and energy dependence, the general of power consumer viscosity is proposed
It reads, the user that autonomous degree of dependence of a certain power consumer in a domain to a certain sale of electricity quotient is defined as power consumer is glued
Property, the viscosity are rather than the comparison decisions of transfer earnings and cost of transfer based on user's reference price decision.Then, it will use
The concept of family viscosity is quantified as some user in a certain amount of time, and selected some sale of electricity quotient is sold the set meal corresponding indentured period
Limit is to carry out analysis discussion.
The specific implementation step of the step 1 is as follows:
Step 11 is started with from the consumptive link of consumer, and analyzing influence user generates the major influence factors of purchase intention.
It is purchase intention that user, which generates the premise that consumption relies on, and purchase intention is mainly by two aspects of price perception and trust-aware
It directly affects, as shown in Fig. 2, its major influence factors includes energy value, brand effect, service level, power quality, conversion
The influence factors such as cost and user preference.
Effect between step 12, each influence factor of analysis.
The price perception of consumer comes from energy value provided by sale of electricity quotient；Trust-aware is the brand from sale of electricity quotient
Effect and public praise.The first assessment using along with satisfaction of consumer, main includes the stealth of service level and desired value
Compare and product (electric energy) quality.After completing consumption experience with a certain supplier, consumer rule of thumb can selectivity
Strong purchase intention is generated to the supplier or some specific product (power supply set meal), i.e. this user produces user preference.
The influence of step 13, the consumption experience of consumer to consumption decision.
Different from online consumption feature for power consumer, the consumption replacement next time sale of electricity chamber of commerce generates certain turn
It changes this into, in the case where conversion cost is higher or user preference is strong, user can be promoted no longer to carry out to a certain extent
Cognition and judgement before the decision of price and satisfaction etc., and a contract time limit sale of electricity set meal is continued directly to, or
The behavior that preference is persistently kept to the set meal of some sale of electricity quotient, is defined as inert behavior, and user's inert behavior finally will be with
The consumption feature of user's viscous behavior shows.
To the great influence of user's decision, the price of clearly power supply set meal is main excellent for step 14, analysis package price
Change object, user's viscosity of single user and the decision matrix of user group are the intermediate link of model.
Step 2, using sticky function, user's viscosity of single user is quantified, and according to multiple sticky functions,
Determine the decision matrix of user group；
In this step, the specific implementation step of the step 2 is as follows:
Step 21 quantifies the viscosity of single user in conjunction with definition.
The concept of user's viscosity is quantified as a certain user selected sale of electricity quotient within the period and is sold power supply set meal
The signing time limit carry out analysis discussion.
Assuming that requirement of the user for power quality is consistent, put aside in the model sale of electricity quotient brand effect and
Influence of the service level to user's viscosity.Assuming that user group is made of N number of user, sale of electricity quotient one shares n retail set meal, then
Shown in sticky function such as following formula (1) of the single user to some sale of electricity quotient object:
In formula, N is potential user's quantity of sale of electricity quotient, i.e. the quantity of all users in sale of electricity quotient operation region；N is should
The set meal number of sale of electricity quotient；SiIndicate the i-th (i=1,2,3, L, N, wherein L indicates median, hereafter similar to be not repeated to retouch
Stating) a user is to the viscosity of sale of electricity quotient；kpiIt is a n dimension group being made of element 0 or 1；J-th in array
Element kpijIndicate selection situation of the user i to j-th of set meal, kpij=1 indicates to select certainly, kpij=0 indicates Negative Selection.
It is assumed that each user can only at most select a set meal, i.e. kpiNon-zero element is no more than 1 in array；T=[T1,T2,...,
Tn], T' is the transposition of T, T thereini(i=1,2,3, L, N) indicates that the contract time limit of i-th of set meal, T indicate all to be optimized
The row vector of the corresponding contract term composition of set meal, T' indicate its transposition.
Step 22, definition and quantization to user group decision matrix.
The decision matrix of user group is the set of all potential single user's decisions of sale of electricity quotient, it is assumed that certain sale of electricity quotient one
It shares N number of potential user, there is n power retailing set meal, then under the specific structure of the decision matrix of user group such as following formula (2):
kpij=0 or 1 (i=1,2 ..., N j=1,2 ..., n)
In formula, Kp is the 0-1 matrix of N × n scale, and what certain a line indicated is selection of some user to n set meal
Situation, what a certain column indicated is decision situation of all potential users to some set meal, and decision is 1 certainly, and negative decision is 0.
Step 3, according to sticky function and decision matrix, establish the total utility function of reaction sale of electricity quotient comprehensive benefit；
In this step, the specific implementation step of the step 3 is as follows:
Step 31, under the background of electricity market, constraint and user group type by electric network composition are limited, Power Generation
It, can be from occupation rate of market and duration if cannot only be achieved the goal with profit maximization to formulate the price of set meal
Angle considers, formulates the power retailing package price with price advantage, realizes the maximization of aggreggate utility.
Step 32, sale of electricity quotient is the profit and competitive advantage for guaranteeing itself in market competition, need to just change tradition and be based on
The profit maximization pricing model of price difference.Establish total utility function such as following formula (3) institute based on user's viscosity and number of users
It states:
In formula, N is the quantity of sale of electricity quotient potential user；NCFor actual user's quantity of sale of electricity quotient；For this
The average market influence power of sale of electricity quotient.
Step 4, using predefined assimilatory coefficient, the influence journey of the price of quantization power supply set meal to the decision of different user
Degree, and according to assimilatory coefficient, establish the decision model of user；
In this step, the specific implementation step of the step 4 is as follows:
Step 41 defines assimilatory coefficient.
The value of assimilatory coefficient receives two parts of coefficient by the dependent coefficient and electric power energy price change of electric power energy
Composition.The consumption for introducing the electric power energy of corresponding industry accounts for the accounting that total energy consumes to measure the degree of dependence of electric power energy,
By taking resident as an example, the ratio of aggregate consumption is accounted for whole nation life power consumption to measure resident to the dependence journey of electric power
Degree；Introduce the consumer price index (consumer price index, abbreviation CPI) of income of residents growth rate and all kinds of commodity
Ratio measure the acceptance level that resident changes commodity price, by taking electricity commodity as an example, the year of the income of residents is utilized to increase
Ratio between rate and water power fuel-based Consumer Prices index measures the acceptance level that resident changes power price.
Assimilatory coefficient γ of the consumer to electricity commodityiIt is as follows:
Wherein, QeIndicate consumer to the total quantity consumed of electricity commodity；QAIndicate the total quantity consumed of consumer's domestic energy；
υiIndicate the income increase rate of consumer i；ηeIndicate the Consumer Prices index of electricity commodity.
In order to make it easy to understand, China totality resident in 2015 can be obtained with the correlation data calculation in 2015 in China below
User is 0.27649 to the assimilatory coefficient average value of power price.The income and the consumption index of consideration city and rural resident
The range of assimilatory coefficient is divided into 0.1~0.5 according to region concrete condition by upper and lower difference, and according to the income level of resident,
5 shelves are classified as, as shown in table 1 below.
| Stepping | Income level of resident | Assimilatory coefficient |
| 1 | R∈(0,0.25Rav] | 0.1 |
| 2 | R∈(0.25Rav,0.75Rav] | 0.2 |
| 3 | R∈(0.75Rav,1.25Rav] | 0.3 |
| 4 | R∈(1.25Rav,1.75Rav] | 0.4 |
| 5 | R∈(1.75Rav,+∞) | 0.5 |
Table 1
Wherein, R indicates the year disposable income of resident；RavIndicate year disposable income per capita.
Step 42 establishes user's decision decision model based on consumer IRP.
Influence model of the assimilatory coefficient to user's decision is as follows:
Wherein, PijIndicate j-th of set meal to the average price of the corresponding year electricity consumption total amount of i-th of user；γiIndicate i-th
The assimilatory coefficient of a user；IRPitIndicate user i in the heart reference price of t moment.
According to the definition of consumer's internal reference price (IRP), refer to consumer according to its actual experience (experience
Include consumption experience before or from external pricing information) reference price for influencing consumer behavior and occurring that generates.It can
It is as follows to obtain IRP model:
IRPit=βiP(t-1)+(1-βi)IRP(t-1)
Wherein, IRPitFor user i t moment heart reference price；IRP(t-1)Indicate all users t moment it
The average value of preceding heart reference price；βiFor the coefficient of sensitivity of price of user i；P(t-1)For the average reference in market before t moment
Price realizes the simplification to model using the average value of potential user IRP here.
Under conditions of ignoring trust-aware, the affirmative decision of consumer is mainly perceived by price and the shadow of user preference
It rings.IRP model is put into the consumer's decision model for considering assimilatory coefficient effect, decision model such as the following table 2 of user can be obtained
It is shown.
Table 2
Wherein, QiFor the year electricity consumption of user i；PunitjFor the energy unit rate of set meal j, wherein the energy unit is taken
Rate refers to the expense that user needs to pay using unit quantity of electricity；PfixedjThe day of set meal j to count constant expense price；aijFor user i
To the preference profile of set meal j, αij=1 indicates preference certainly, αij=0 indicates negative preference.
Step 5, according to total utility function, decision model and improve particle swarm algorithm optimizing, determine optimal package price.
In this step, which mainly includes following two parts:
5.1, particle swarm algorithm is improved.
Compared with most evolution algorithms, the advantage of PSO algorithm be by " cooperation " between individual rather than " survival of the fittest " come
Optimal solution is searched, each of group individual has memory to the path before oneself, is also based on consumption in conjunction with the characteristics of IRP
What the experience of person determined, so selection particle swarm algorithm carries out subregional best price search to entire sale of electricity set meal.With biography
The particle swarm algorithm of system is different, and method of the invention improves traditional particle swarm algorithm in terms of following 3:
1), the search of the single array single area of individual parameter in population is improved to more as unit of cell array
Global optimum's individual in the region of search of dimension group multiple regions specifically possesses one in the set of n power supply set meal
Meal system, individual configurations are expressed as the cell array of the size of n × 2, form entire population further according to selected population quantity.
2), in initialization, selection is started with from existing package price, because certain during set meal before is formulated
The influence of cost factor is considered, the factor of cost can be considered to the price of set meal after avoiding optimizing wherein is detached from this way
The constraint of cost improves the reasonability and feasibility of set meal after optimization.During price search, not due to cost constraint
Together, n set meal is corresponded into different region of search and carries out search simultaneously.
3), introduce cistern of chiasma, imitate the exchange of information during bird is looked for food so that the individual in population not only by
The influence of optimum individual also suffers from the influence of other average individuals, improves the probability for searching global optimum.Due in population
The path of more excellent individual is important the optimum search of entire population and the influence of convergence process, so being intersected
In the process, it has only selected the individual of population scale half quantity to carry out, local optimal, limitation can be fallen into a certain extent
The individual intersected two-by-two, but the site of the intersection of two individuals is randomly generated.By the crossover rule of such as following figure, may be implemented more excellent
Mutual supplement with each other's advantages between individual allows to preferably restrain.
5.2, above-mentioned steps 5 are mainly made of 3 links: the generation and amendment, calculating of initialization link, decision matrix
Target function value.Firstly, initialization link includes:
Step 51, price, customer parameter and the preference coefficient of each power supply set meal are initialized, the user
Parameter includes the year electricity consumption and coefficient of sensitivity of price of user；
In this step, as shown in figure 3, the process of the initialization includes the initialization of the price of power supply set meal, Yong Hucan
The initialization of several initialization and preference coefficient, and the link is also the initialization of population position and speed, wherein set meal
The initialization of price is also the initialization of population position, and speed parameter refers to that the parameter of the search speed of population and the speed refer to
Be update step-length individual in population a range intervals, speed difference will affect after an iteration individual to next
A updated position (price for herein referring to power supply set meal).
1, the price initialization of power supply set meal.In order to guarantee the structure of power supply set meal system while simplify program, powering
Cell array structure is quoted when package price initializes, and is a cellular by every group of complete set meal.
2, customer parameter initializes.Assuming that the initial data of each potential user known, the initial data include year electricity consumption,
The ginseng of the time limit of signing, the cost of transfer of user and t moment user's heart that cut-off t moment user and sale of electricity quotient have completed
Price is examined, customer parameter mainly includes 2 after initialization, respectively the year electricity consumption and coefficient of sensitivity of price of user, in addition, root
The coefficient of sensitivity of price β of the user is estimated according to the electricity consumption of user order locating in potential useri。
3, preference coefficient is initialized.Consider the cost of transfer of user and the sale of electricity quotient history service contract total duration and user
Double factor obtain synthesis preference coefficient of the user to sale of electricity quotient (preference at this time be the feature as user, is only used
Judgement of the family to sale of electricity quotient's yes/no preference does not refine to some specific power supply set meal).
In addition, the initialization of the preference coefficient and coefficient of sensitivity of price of user be by means of the level of user's year electricity consumption,
Assuming that the big user price sensitivity coefficient of electricity consumption is smaller, while it is higher a possibility that there are preferences.
Secondly, user's decision link mainly includes two processes: the generation and amendment of decision matrix, so far initialization is tied
Beam, into iteration, the generation method of the decision matrix is according to IRP comparison result, the decision matrix of initialising subscriber, the generation
The specific implementation step of method is as follows:
Step 52, using the price and IRP model of the power supply set meal, the heart reference price of the user is calculated；
In this step, as shown in figure 3, preference matrix of the decision matrix of user based on a upper link user it is initial
Change and formed, the initialization of preference matrix is based on preference coefficient initialization, the ring initialized for the user that preference coefficient is non-zero
Section is randomly assigned the set meal of a preference for it, i.e., corresponding element is set as " 1 " in preference matrix correspondence.Then currently to supply
The price of electric set meal and the heart reference price that user is calculated according to the calculation formula of IRP model.
Step 53, using the calculation formula of the assimilatory coefficient, the assimilation system that custom power is consumed under current environment is calculated
Number；
Step 54, after obtaining the assimilatory coefficient of user's heart reference price and the current environment, using described
Assimilatory coefficient and the decision model determine that the user to the affirmative decision or negative decision of each power supply set meal, finally obtains
To the decision matrix of the user group；
In this step, referring to table 1 or similar to the data in table 1 under the conditions of the assimilatory coefficient of certain level according to table
Model in 2 is compared, and obtains single user to the affirmative decision of each set meal or negative decision, according to certain arrangement
The decision matrix of the sequence composition such as potential user group of formula (2), wherein the decision in the decision matrix is the use in step 2
Family group decision matrix, decision matrix are Kp above.
The specific implementation step of the modification method of the decision matrix is as follows:
Step 55, determine that each user affirms in the user there are in the case where preference from meeting with the presence or absence of preference
The power supply set meal of user preference is chosen in multiple power supply set meals of decision, if it does not exist, from the multiple power supplies for meeting decision certainly
Optimal power supply set meal is chosen in set meal；
In this step, it to guarantee that potential each user at most selects a set meal, needs to be modified matrix.With
It is separation whether there is or not preference, last decision amendment is carried out respectively to two class users, for there are the user of preference, first random choosings
The set meal for selecting preference other is determined on the basis of randomly selected preference set meal meets IRP and judges if existed simultaneously certainly
Plan, no matter whether the set meal of preference is optimal, the set meal of preferential selection preference；For the user of not preference, work as presence
Then according to the optimal selection (P of the average price of the corresponding all set meals of the user when multiple decisions certainlyRijThe minimum of value
Set meal selected).
In addition, the calculation formula of average price indicates are as follows:
PRij=(Qi×Punitj+365×Pfixedj)/Qi
In formula, PRijIndicate average price of the user i to the every degree electricity for meeting the power supply set meal j with reference to decision certainly.
It mainly include two processes finally, calculating in the target function value link of total utility: user's viscosity and fitness
The calculating of objective function and the search and intersection of optimal population, the link are related to the update of population optimal location.
Step 56, using the calculation formula of the total utility function, the value of the corresponding fitness function of each individual is calculated,
In, value of the value of the total utility function as the fitness function；
In this step, on the basis of above-mentioned decision matrix, user's viscosity and number of users are easy to get according to formula (1)
Amount obtains the value of total utility function further according to formula (3), that is, utilizes the calculation formula of the total utility function, calculate each individual
The size of corresponding fitness function, total utility function select total utility function as solution link as objective function
Fitness function solves the value of the total utility function.
Step 57, the optimal and individual history of Population Regeneration is optimal, and population is merged into cistern of chiasma and executes crossover operation,
In, price corresponding to the value of maximum target function in the combination of the optimal price for power supply set meals all in an iteration of population
Combination, price corresponding to the value of maximum target function after the optimal current iteration power supply package price update for cut-off of individual history
Combination；
In this step, the optimal and individual history of Population Regeneration is optimal, and population is merged into execute in cistern of chiasma and intersects behaviour
Make, and be that individual between population is intersected, wherein population it is optimal for individual mid-range objectives all in an iteration most
Close, that is, price combination corresponding to objective function maximum, is simultaneously in the combination of all power supply package prices loaded
Carve the comparison between Different Individual；The optimal end individual for cut-off current iteration of individual history near the position of close-target,
I.e. cut-off current iteration package price update after the maximum corresponding price combination of objective function, be different moments same individual into
Capable comparison.
Further, since the path of more excellent individual is to the optimum search of entire population and the influence of convergence process in population
Important, so individual is ranked up according to fitness function value, has only been selected in the top during being intersected
The individual of half carry out, and intersect site and be randomly generated, can prevent from falling into a certain extent local optimal.
In addition, being intersected according to following crossover rule, which is: assuming that the number of individuals in population is Nt, will
All individuals successively have 1 number to Nt, after fitness function solves in an iteration, according to the size of fitness function to individual
Descending sequence is carried out, is intersected only in preceding NtOccur in/2 individuals.To number as No. i individual and number is Nt/2-i+
1 individual is intersected, and is randomly generated and is intersected site, the exchange that last two individuals align the information after site,
The part package price loaded on individual is carried out to the exchange on same loci.
Step 58, if meeting stop condition, search stops, and exports result；Otherwise it returns to the step 52 to continue searching, i.e.,
Judge whether the number of iterations and precision meet the requirements, jumps out or continue cycling through.
In addition, 3 set meals of fourth quarter in 2016 of the SSE company of one of big sale of electricity company, Britain 6 of selection below
For data (1 year, 2 years, variable-rate (1 month)) as the initial set meal before optimization, specific data are as shown in table 3 below.
Table 3
Based on consumer price decision model, under conditions of considering assimilatory coefficient, 3400 potential users are to initial set meal
Selection situation it is as shown in table 4 below.
| Classification | Set meal one | Set meal two | Set meal three | It amounts to |
| Number of users (family) | 691 | 1708 | 1001 | 3400 |
Table 4
It can be seen from the results above that the design of set meal may be implemented on the basis of 0.3 user's assimilatory coefficient it is potential
The all standing of user group.But from the point of view of the distribution of number of users, user is then more likely to select set meal two (contract period is 1 year),
It can be seen that original package in the case where guaranteeing the sale of electricity quotient market coverage, does not account for the market duration of sale of electricity quotient to total
The influence of effectiveness.
On the basis of not changing set meal number, the set meal contract time limit and penalty, in set meal unit rate and
Nontraffic sensitive optimizes.It is obtained after adjustment maximum number of iterations and population scale multiple combinations, when maximum number of iterations is
50, when population scale is 40, it is as follows to obtain optimal set meal result under conditions of assimilatory coefficient is 0.3 for market reference price 14
Shown in table 5.
Optimization front and back potential user is to the specific choice situation of set meal are as follows: user to the selection quantity of set meal one by optimizing before
691 families be reduced to 511 families；Set meal two is reduced to 1151 families by 1708 families；Set meal three increases to 1651 families by 1001 families；Although
Total number of users is reduced to 3313 families by 3400 families before optimizing, but the total utility function of sale of electricity quotient is improved extremely by 3883
4462.It realizes in the case where small range loss occurs in number of users, significantly improves target total utility functional value, realize
Sale of electricity quotient's total benefit maximizes.
The above is only a specific embodiment of the invention, is made skilled artisans appreciate that or realizing this hair
It is bright.Various modifications to these embodiments will be apparent to one skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and applied principle and features of novelty phase one herein
The widest scope of cause.
Claims (10)
1. a kind of optimization method based on the power supply set meal for improving particle swarm algorithm characterized by comprising
The factor of influence user's viscosity under step 1, analysis Power Market, the price for the set meal that will power is as optimization pair
As, wherein the factor includes the price of the power supply set meal；
Step 2, using sticky function, user's viscosity of single user is quantified, and according to multiple sticky functions, determines
The decision matrix of user group；
Step 3, according to the sticky function and the decision matrix, establish the total utility function of reaction sale of electricity quotient comprehensive benefit；
Step 4, using predefined assimilatory coefficient, quantify the price of the power supply set meal to the influence journey of the decision of different user
Degree, and according to the assimilatory coefficient, establish the decision model of user；
Step 5, according to the total utility function, the decision model and the improvement particle swarm algorithm optimizing, determine optimal set
Meal price.
2. the optimization method of power supply set meal according to claim 1, which is characterized in that in the step 2, the viscosity
Function representation are as follows:
In formula, N is the quantity for all users that sale of electricity quotient manages in region；N is of the power supply set meal of sale of electricity quotient publication
Number；SiIndicate i-th of user to user's viscosity of the sale of electricity quotient, wherein 0 < i≤N, and i is positive integer；kpiIt indicates by member
One n dimension group of 0 or 1 composition of element；kpijIndicate user i to the selection situation of j-th of set meal of powering, kpij=1 indicates affirmative
Decision, kpij=0 indicates negative decision；The transposition of T' expression T, wherein T=[T1,T2,...,Tn], TiIndicate i-th of power supply set
The contract time limit of meal.
3. the optimization method of power supply set meal according to claim 2, which is characterized in that in the step 2, the decision
Matrix is expressed as:
In formula, Kp indicates the 0-1 matrix of N × n scale.
4. the optimization method of power supply set meal according to claim 3, which is characterized in that in the step 3, total effect
With function representation are as follows:
In formula, NCIndicate that the sale of electricity quotient manages actual user's quantity in region；Indicate the sale of electricity quotient
Average market influence power.
5. the optimization method of power supply set meal according to claim 1, which is characterized in that in the step 4, the assimilation
Coefficient is expressed as:
In formula, γiIndicate the assimilatory coefficient of i-th of user；QeIndicate the user to the total quantity consumed of electricity commodity；QAIt indicates
The total quantity consumed of the domestic energy of the user；υiIndicate the income increase rate of the user i；ηeIndicate the electricity commodity
Consumer Prices index.
6. the optimization method of power supply set meal according to claim 1, which is characterized in that described to determine in the step S4
Plan model is expressed as:
In formula, kpij=1 indicates decision certainly, kpij=0 indicates negative decision；
Constraint condition:
Certainly the constraint condition of decision indicates are as follows:
In formula, QiFor the year electricity consumption of user i；PunitjFor the energy unit rate for the set meal j that powers；PfixedjFor power supply set
The day of meal j counts constant expense price；IRPitIndicate the user i obtained using IRP model in the heart reference price of t moment；aij
It is the user i to the preference profile of the power supply set meal j, aij=0 indicates negative preference；
It negate that the constraint condition of decision indicates are as follows:
In formula, aij=1 indicates preference certainly.
7. the optimization method of power supply set meal according to claim 6, which is characterized in that the IRP model is expressed as:
IRPit=βiP(t-1)+(1-βi)IRP(t-1)
In formula, IRPitFor user i t moment heart reference price；IRP(t-1)Indicate the heart before t moment of all users
The average value of reference price；βiIndicate the coefficient of sensitivity of price of the user i；P(t-1) indicate t moment before market average ginseng
Examine price.
8. the optimization method of power supply set meal according to claim 7, which is characterized in that the step 5 includes:
Step 51, price, customer parameter and the preference coefficient of each power supply set meal are initialized, wherein the use
Family parameter includes the year electricity consumption and coefficient of sensitivity of price of user；
Step 52, the price using the power supply set meal and the IRP model, calculate the heart reference price of the user；
Step 53, using the calculation formula of the assimilatory coefficient, the assimilatory coefficient that custom power is consumed under current environment is calculated；
Step 54, after obtaining the assimilatory coefficient of user's heart reference price and the current environment, the assimilation is utilized
Coefficient and the decision model determine that the user to the affirmative decision or negative decision of each power supply set meal, finally obtains institute
State the decision matrix of user group；
Step 55, determine that each user affirms decision from meeting in the user there are in the case where preference with the presence or absence of preference
Multiple power supply set meals in choose user preference power supply set meal, if it does not exist, from meet certainly decision multiple power supply set meals
It is middle to choose optimal power supply set meal；
Step 56, using the calculation formula of the total utility function, the value of the corresponding fitness function of each individual is calculated, wherein
Value of the value of the total utility function as the fitness function；
Step 57, the optimal and individual history of Population Regeneration is optimal, and population is merged into cistern of chiasma and executes crossover operation, wherein
Price group corresponding to the value of maximum target function in the combination of the optimal price for power supply set meals all in an iteration of population
It closes, price group corresponding to the value of maximum target function after the optimal current iteration power supply package price update for cut-off of individual history
It closes；
Step 58, if meeting stop condition, search stops, and exports result；Otherwise the step 52 is returned to continue searching.
9. the optimization method of power supply set meal according to claim 8, which is characterized in that from the multiple confessions for meeting decision certainly
Optimal power supply set meal is chosen in electric set meal includes:
Calculate the average price of every degree electricity of all power supply set meals for meeting decision certainly；
Select the smallest power supply set meal of average price of every degree electricity as optimal power supply set meal.
10. the optimization method of power supply set meal according to claim 9, which is characterized in that the calculating of the average price is public
Formula indicates are as follows:
PRij=(Qi×Punitj+365×Pfixedj)/Qi
In formula, PRijIndicate average price of the user i to the every degree electricity for meeting the power supply set meal j with reference to decision certainly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621364.6A CN109685197A (en) | 2018-12-28 | 2018-12-28 | A kind of optimization method based on the power supply set meal for improving particle swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621364.6A CN109685197A (en) | 2018-12-28 | 2018-12-28 | A kind of optimization method based on the power supply set meal for improving particle swarm algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109685197A true CN109685197A (en) | 2019-04-26 |
Family
ID=66190167
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811621364.6A Pending CN109685197A (en) | 2018-12-28 | 2018-12-28 | A kind of optimization method based on the power supply set meal for improving particle swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109685197A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112950407A (en) * | 2021-03-26 | 2021-06-11 | 昆明电力交易中心有限责任公司 | Electricity retail customer viscosity analysis method and system for electricity selling company |
| CN112967108A (en) * | 2021-03-09 | 2021-06-15 | 昆明电力交易中心有限责任公司 | BP-ANN-based electric power retail package searching and sequencing simulation system and method |
-
2018
- 2018-12-28 CN CN201811621364.6A patent/CN109685197A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112967108A (en) * | 2021-03-09 | 2021-06-15 | 昆明电力交易中心有限责任公司 | BP-ANN-based electric power retail package searching and sequencing simulation system and method |
| CN112967108B (en) * | 2021-03-09 | 2023-11-03 | 昆明电力交易中心有限责任公司 | BP-ANN-based power retail package search ordering simulation system and method |
| CN112950407A (en) * | 2021-03-26 | 2021-06-11 | 昆明电力交易中心有限责任公司 | Electricity retail customer viscosity analysis method and system for electricity selling company |
| CN112950407B (en) * | 2021-03-26 | 2023-07-04 | 昆明电力交易中心有限责任公司 | Viscosity analysis method and system for electric power retail customers facing electric power company |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ginting et al. | Product development with quality function deployment (QFD): a literature review | |
| CN110728410A (en) | Load aggregator economic scheduling method considering demand response flexibility and uncertainty | |
| CN104700158B (en) | A kind of energy management method of distribution garden and system | |
| CN109347149A (en) | Micro-capacitance sensor energy storage dispatching method and device based on depth Q value network intensified learning | |
| CN106484512B (en) | The dispatching method of computing unit | |
| CN107330549A (en) | Multi-energy complementary micro-grid energy management method containing multiagent | |
| Gomes et al. | A multiple objective evolutionary approach for the design and selection of load control strategies | |
| CN109741120B (en) | Product deep personalized customization method for user requirements | |
| CN108734396A (en) | A kind of virtual plant method for optimizing scheduling bidded and provided multiple forms of energy to complement each other based on Demand-side | |
| CN101794424A (en) | Self-adaptive unified power market transaction method | |
| CN107451931A (en) | The Optimization Scheduling of home intelligent power equipment | |
| CN109685197A (en) | A kind of optimization method based on the power supply set meal for improving particle swarm algorithm | |
| CN109840808A (en) | A kind of methodology based on the load aggregation quotient's profit for improving Shapley value | |
| CN112232863B (en) | Virtual resource management method and system based on cloud edge-side cooperation | |
| CN109919485A (en) | A kind of Multipurpose Optimal Method of the Water Resources Allocation based on NSGA-II | |
| CN109461019A (en) | A kind of dynamic need response pricing method based on Fuzzy Reinforcement Learning | |
| CN108133394A (en) | Consider the controllable burden demand response menu pricing method of consumer's risk preference | |
| CN117117878A (en) | Power grid demand side response potential evaluation and load regulation method based on artificial neural network and multi-agent reinforcement learning | |
| Wu et al. | Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade | |
| CN111080014A (en) | Load curve optimization method based on load aggregator non-cooperative game | |
| CN110135028A (en) | House Intelligent Energy router integration requirement response method based on Load aggregation quotient | |
| CN114188940B (en) | Active power distribution network scheduling method considering benefits of multiple subjects | |
| Samadi et al. | Stochastic demand response management using mixed-strategy Stackelberg game | |
| CN105071421B (en) | Office Building Energy Consumption management method | |
| CN113610641A (en) | Park multifunctional operator transaction decision method considering data center demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190426 |