CN112366699A - Household energy double-layer optimization method for realizing interaction between power grid side and user side - Google Patents

Household energy double-layer optimization method for realizing interaction between power grid side and user side Download PDF

Info

Publication number
CN112366699A
CN112366699A CN202011209070.XA CN202011209070A CN112366699A CN 112366699 A CN112366699 A CN 112366699A CN 202011209070 A CN202011209070 A CN 202011209070A CN 112366699 A CN112366699 A CN 112366699A
Authority
CN
China
Prior art keywords
power
user
electricity
peak
load
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
Application number
CN202011209070.XA
Other languages
Chinese (zh)
Inventor
傅质馨
李紫嫣
朱俊澎
袁越
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hohai University HHU
Original Assignee
Hohai University HHU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hohai University HHU filed Critical Hohai University HHU
Priority to CN202011209070.XA priority Critical patent/CN112366699A/en
Publication of CN112366699A publication Critical patent/CN112366699A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a family energy double-layer optimization method for realizing interaction between a power grid side and a user side, which comprises the following steps: reading a daily load curve of a user, reducing the dimension of high-dimensional load data based on the characteristic indexes, and acquiring a dimension reduction curve; calculating the membership degree of the dimensionality reduction curve based on an FCM fuzzy clustering algorithm to complete the classification of daily load curves; classifying the residential users, and designing an electric power package for each type of residential users; simulating a day-ahead load curve, and performing real-time monitoring and load decomposition on the user household appliance by using a non-invasive optimization model; and dynamically adjusting the peak-to-valley electric quantity coefficient, and switching on and off the equipment in real time according to the dynamic priority of the household appliance. The method realizes the dispatching of the electric power package which gives consideration to time-of-use electricity price and an incentive subsidy mechanism to the specific load equipment of the home users, is used for improving the peak-valley difference of a resident load curve, improves the interaction degree of the electricity utilization end and the power supply end, and enables the resident users to participate in the optimized dispatching of the power grid more actively.

Description

Household energy double-layer optimization method for realizing interaction between power grid side and user side
Technical Field
The invention belongs to the field of power optimization scheduling, relates to a family energy optimization method, and particularly relates to a family energy double-layer optimization method for realizing interaction between a power grid side and a user side.
Background
In most of the existing researches, the time-of-use electricity price is only fixed electricity price, and the time-of-use electricity price mechanism cannot reflect the interactive relation between a power grid and users and is not enough to mobilize the enthusiasm of residential users for participating in power grid peak shaving. Therefore, related departments and experts and scholars provide an incentive mechanism for promoting resident users to participate in power grid peak shaving, and certain subsidies are provided when residents reduce power consumption requirements. With the popularization of the smart electric meters, the non-invasive load decomposition technology is gradually and widely applied to the smart home optimized scheduling in the household daily life power utilization.
However, if only the intelligent scheduling condition of the load is considered, the environment assumption of the time-sharing electricity price is single, and the interactive relation of the power supply and utilization end is difficult to embody. A new technical solution is needed to solve this problem.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, a household energy double-layer optimization method for realizing interaction between a power grid side and a user side is provided, and is used for improving the peak-valley difference of a resident load curve, improving the interaction degree between a power utilization end and a power supply end and enabling resident users to actively participate in power grid ground optimization scheduling.
The technical scheme is as follows: in order to achieve the above purpose, the present invention provides a household energy double-layer optimization method for realizing interaction between a power grid side and a user side, comprising the following steps:
s1: reading a daily load curve of a user, reducing the dimension of high-dimensional load data based on the characteristic indexes, and acquiring a dimension reduction curve;
s2: calculating the membership degree of the dimensionality reduction curve based on an FCM fuzzy clustering algorithm, and finishing the classification of the daily load curve according to the membership degree;
s3: classifying the residential users according to the classification result of the daily load curve, and designing an electric power package for each type of residential users to obtain a peak-valley electric quantity coefficient standard value and an electric price discount coefficient which meet the best benefit of a power generation side, a power grid side and an environment side and the minimum electric charge expense of the users;
the effect of the discount coefficient is: when the load curve of the user meets the standard value of the peak-valley electric quantity coefficient, a certain electric price discount is given to the user as a subsidy, and the discount is made on the basis of the electric charge expenditure before the electric power package is adopted. That is, the comfort of the user is slightly lowered, and the electricity charge of the user is also reduced.
The power package designed on the outer layer comprises a peak-valley electric quantity coefficient and a power price discount coefficient, and when the inner layer is optimized, the peak-valley electric quantity coefficient is used as a constraint condition to constrain the scheduling of the intelligent household appliances.
S4: simulating a load curve before the day according to the peak-valley electric quantity coefficient, and carrying out real-time monitoring and load decomposition on the user household appliance by using a non-invasive optimization model;
s5: judging whether an unplanned power utilization behavior occurs, if not, executing power utilization according to an original plan, namely a load simulation curve before the day; if yes, calculating a peak-valley electric quantity coefficient containing the unplanned power consumption, judging whether the peak-valley electric quantity coefficient exceeds a standard value, if not, executing the power consumption according to the original plan, if so, dynamically adjusting the peak-valley electric quantity coefficient according to the dynamic priority of the user household appliance until the peak-valley electric quantity coefficient does not exceed the standard value; and giving the corresponding electricity price discount coefficient to the user when the peak-valley electricity quantity coefficient standard value is met.
Further, the method for calculating the membership degree of the dimensionality reduction curve in the step S2 includes:
suppose the data set to be clustered is X ═ X1,x2,...,xnDimension reduction is carried out according to the characteristic indexes to obtain a data set Y ═ Y1,y2,...,ynIn which y isj＝(yj1,yj2,...,yj6)TEach sample is correspondingly provided with 6 characteristic attributes as a characteristic attribute vector; y iskjAs feature vector ykThe j-th dimension of the feature quantity; the FCM algorithm uses a fuzzy partition matrix to carry out clustering on the resultsLine representation, i.e.
Figure BDA0002758162420000021
Wherein c is the number of quasi-classes; u. ofijThe j (j is 1,2, …, n) th data sample belongs to the membership degree of the i (i is 1,2, …, c) th class, and the following conditions are satisfied: to pair
Figure BDA0002758162420000022
To pair
Figure BDA0002758162420000023
Figure BDA0002758162420000024
Wherein
Figure BDA0002758162420000025
Representing any i and any j.
In the fuzzy c-clustering algorithm, uijThe probability that a representative membership, i.e. a sample j, belongs to the ith class must be between 0 and 1. And the jth sample must belong to a class, so its sum of membership degrees for each class is 1.
Further, in the step S2, a Silhouette index is used to evaluate the clustering quality, and the optimal clustering number of the daily load curve of the residents is determined; the Silhouette index is defined as follows:
given a total number of samples n, a number of pseudo-classifications
Figure BDA0002758162420000026
For sample j in class i, define di(j) Representing the in-class compactness degree as the average distance between the sample j and all other samples in the class, wherein the smaller the value of the in-class compactness degree is, the more compact the in-class compactness is; (ii) a Definition of do(j) And representing the dispersion degree among classes for the average distance from the sample j to all the samples which are not in the same class, wherein the larger the value of the dispersion degree among the classes is, the more the dispersion among the classes is. Silhouette index J of sample JSil(j) Expressed as:
Figure BDA0002758162420000027
the Silhouuette index mean J of all samplesSilmeanExpressed as:
Figure BDA0002758162420000031
JSilmeanthe number of classes corresponding to the maximum value is the optimal clustering number.
Further, the design method of the power package in step S3 is as follows: establishing an electric power package model aiming at daily load, wherein the electric power package comprises a peak-valley electric quantity coefficient standard value and an electric price discount coefficient, and optimizing the electric power package of a resident user by adopting a genetic algorithm, specifically comprising the following steps:
the electricity fee expenditure is expressed as:
Figure BDA0002758162420000032
in the formula:
Figure BDA0002758162420000033
and
Figure BDA0002758162420000034
respectively the daily initial total electric charge of the class II user group and the total electric charge after the electric power set is carried out; bjA discount coefficient for electricity price; f. oftElectricity prices for a period of t; etaij,tThe proportion of the electricity consumption in the time period t of each day to the total daily electricity consumption after a package j is selected for a typical user i; p is a radical ofijProbability of choosing package j for a fully rational representative user i under ideal conditions;
Figure BDA0002758162420000035
the total daily electricity consumption of the ith user group is obtained;
wherein:
Figure BDA0002758162420000036
in the formula: u shapeijThe satisfaction degree of the user after the set of meal is used is shown, the satisfaction degree comprises the satisfaction degree of electricity expense and the satisfaction degree of an electricity consumption mode measured by daily load curve offset, and the satisfaction degree is specifically as follows:
Figure BDA0002758162420000037
in the formula: u shapeij,1Selecting the electricity expense satisfaction degree of the package j for the typical user i;
Figure BDA0002758162420000038
in the formula: u shapeij,2Selecting the electricity utilization satisfaction degree of a package j for a typical user i; etai,t' is the initial power usage proportion of a typical user i in a period t per day; r isiAnd q isiIs a parameter related to the satisfaction degree of the electricity using mode;
Uij＝αiUij,1+(1-αi)Uij,2 αi∈[0,1] (8)
in the formula: alpha is alphaiRepresenting the weight given to the satisfaction degree of the typical user on the electricity fee expenditure;
the electric power selling income reduced after the electric power package is pushed out by the power grid side, namely the electric power expense reduced by the user, and the cost for pushing out the electric power package by the power grid side comprises the marketing and management expense C of the electric power packaget；
The power generation side benefits include that the cost of newly increased power generation capacity can be avoided, the power generation cost of a high-cost unit can be avoided, and the abnormal start-stop cost of the unit can be avoided, and are expressed as follows:
Figure BDA0002758162420000041
in the formula: a. theTThe cost per kilowatt capacity can be avoided; a. thegThe unit power generation cost of a high-cost unit; a. thesThe cost is the abnormal starting and stopping cost each time; n issThe number of abnormal starting and stopping times is reduced; k is a radical of1As a percentage of planned reserve capacity; k is a radical of2Loss coefficients of the transmission and distribution network; k is a radical of3The plant power rate; the delta P is the potential peak load reduction of the power package; the delta Q is the total electricity consumption of the load of residents in the peak period for implementing the reduction of the electric power combo;
the benefits of the power grid side include the avoidable power grid investment cost, the system reliability benefits, and the avoidable electricity cost, which are expressed as:
Figure BDA0002758162420000042
in the formula: a. thepThe unit average cost of the transmission line, the transformer substation and the supporting facilities thereof is calculated; a. thefA difference in the cost of electricity purchased for peak and valley charges per kilowatt-hour; a. theVOLLThe value of power loss load; a. theSMPIs the marginal cost of electricity; p is a radical ofLOLPThe power system load loss probability;
the environmental benefit is expressed as:
Figure BDA0002758162420000043
in the formula: qAAnd Δ QARespectively the total power generation amount and the reduced power generation amount after the package is implemented;
Figure BDA0002758162420000044
and
Figure BDA0002758162420000045
the emission reduction coefficients and the emission reduction values of carbon dioxide, sulfur dioxide and nitric oxide are respectively; delta xi is the percentage point of the improvement of the postprandial load rate of the electric power set; c. CgThe coal consumption is supplied to the coal-fired unit;
Figure BDA0002758162420000046
the load rate is the unit coal consumption reduction rate of the coal-fired unit corresponding to 1 percent point of the load rate increase; for simplificationExpression of using rΣRepresents the total value of greenhouse gas emission reduction per reduction of unit power generation;
in summary, the optimization model of the electric power package design of the residential users is shown as formula (12):
Figure BDA0002758162420000047
further, the obtaining manner of the peak-to-valley electric quantity coefficient standard value and the discount coefficient of the incentive mechanism in step S3 is as follows: and selecting a typical daily load curve in each type of residential users as a research object, satisfying the optimization model by adopting a genetic algorithm, and obtaining an individualized power package, namely obtaining a peak-to-valley electric quantity coefficient standard value and a discount coefficient of an incentive mechanism.
The peak-valley electric quantity coefficient is a precondition for the residents to enjoy the discount of the electricity price, and is properly adjusted according to the peak-valley coefficient value before each type of residents use the package, so although not given in a specific formula, the peak-valley electric quantity coefficient is a precondition of a model, and is a conditional statement of while in programming.
Further, the method for non-invasively monitoring and load-decomposing the user appliance in real time in step S4 includes: a home energy management framework that simulates a home user, comprising: the household electric appliance comprises daily household electric appliances such as a washing machine, a dish-washing machine, a dust collector, a humidifier 1, a humidifier 2, a humidifier 3, a water dispenser 1, a water dispenser 2, an iron, a water heater, an air conditioner, an electric oven and the like. The using requirements of the electric equipment of each family and the using characteristics of the equipment are considered, the day-ahead load of each family is simulated through a genetic algorithm, the running time of each equipment is used as a decision variable, and the peak-valley electric quantity coefficient is used as an objective function, so that the running condition of the household electric equipment is obtained.
Due to the randomness of the electricity utilization of the user, an unplanned electricity utilization situation can occur, and therefore, the real-time monitoring and load decomposition of the household load can be better selected. Real-time power utilization information is obtained through a non-intrusive load monitoring and decomposing method, active power is selected as a load mark, and load decomposition is carried out by combining operation period information. The NILMD is an important component of an intelligent power utilization system, and compared with the traditional intrusive load monitoring, the non-intrusive load monitoring can acquire the energy consumption condition of each load device only by installing a load monitoring device at a user power house. The load curve is decomposed into the running conditions of the various household appliances without depending on the installation of a sensor on each household appliance for monitoring.
Further, in step S5, a dynamic priority model is used to obtain dynamic priorities of the user appliances, where the dynamic priority model includes a water heater model and an air conditioner model, and the dynamic priority model specifically includes the following steps:
the water heater model:
when the water heater is in an opening state, a closing state and a water using state in a time interval, the water temperature at the moment is respectively shown as formulas (13) to (15), and the constraint condition is shown as formula (16):
Figure BDA0002758162420000051
Figure BDA0002758162420000052
TWH,t＝[TWH,t-1·(M-dn+1)+Ten·dn+1]/M (15)
TWH,t∈[TWH,s-ΔTWH,TWH,s] (16)
in the formula: t isenRepresents the ambient temperature, i.e., the cold water temperature; τ represents a time interval; m represents the capacity of the water heater; rhRepresenting the thermal resistance coefficient of the water heater; chRepresenting the heat capacity of the water heater; qhRepresenting the water heater power; dn+1Is shown during a time period t-1, t]The amount of cold water added internally; t isWH,sRepresenting a maximum water temperature set value; delta TWHSetting a range for the water temperature;
the water heater operating state is related to the water temperature setting, when the water temperature is higher than the maximum temperature TWH,sWhen the water heater is powered off; when the temperature is lower than the lowest temperature, the water heater is electrified; when the water heater is in the set range, the water heater keeps the original stateA state; water heater control model and comfort index K thereofWH,tThe following were used:
Figure BDA0002758162420000061
Figure BDA0002758162420000062
in the formula: sWH,tThe working state of the water heater is in a t period (the value is 0 to indicate power failure, and the value is 1 to indicate power on); t isWH,tWater temperature is t time period;
KWH,tis the difference between the current water temperature and the highest set value after per unit, the higher the water temperature is, the comfort index KWH,tThe larger the user is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And during the demand response period, controlling the power-on and power-off states of the water heaters according to the priority of the water heaters.
An air-conditioning model:
when the air conditioner is in [ t-1, t ]]Room temperature T at time T in the on and off statesAC,tThe running constraint conditions are shown as formulas (19) and (20) and as formula (21);
Figure BDA0002758162420000063
Figure BDA0002758162420000064
T∈[TAC,s,TAC,s+ΔTAC] (21)
in the formula: raRepresenting the thermal resistance coefficient of the air conditioner; caRepresenting air conditioner heat capacity; qaRepresenting air conditioner power; t isoutRepresents the ambient temperature; t isAC,sRepresents the lowest room temperature set value; delta TACSetting a range for room temperature;
assuming that the air conditioner is operated in a cooling mode, the air conditioner operation state is related to the room temperature setting, and when the room temperature is higher than the maximum value,powering on an air conditioner; when the air temperature is lower than the minimum value, the air conditioner is powered off; when the air conditioner is in the set range, the air conditioner keeps the original state; control model and comfort index K thereofAPPThe calculation is as follows:
Figure BDA0002758162420000065
Figure BDA0002758162420000066
in the formula, SAC,tThe working state of the air conditioner is t time (the value is 0 to indicate power-off, and the value is 1 to indicate power-on); t isAC,tRoom temperature for a period of t;
KAC,tis the difference between the current room temperature and the lowest set value after per unit, the higher the room temperature is, the comfort index KAC,tThe larger the user is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And during the demand response period, controlling the power-on and power-off states of the air conditioners according to the priorities of the air conditioners.
The family energy double-layer optimization method is divided into an outer layer and an inner layer, the two-layer hierarchical structure is provided, and the outer layer model and the inner layer model are provided with respective decision variables, constraint conditions and objective functions. The optimization result of the outer layer decision variable is used as the constraint condition of the inner layer, and the optimization result of the inner layer objective function is used as a parameter to be transmitted to the outer layer, so that the inner layer problem of the double-layer optimization model is equivalent to parameter planning for the outer layer optimization. On the basis of carrying out cluster analysis on daily load curves, an outer layer of the double-layer optimization method designs an electric power package containing excitation subsidies and peak-valley electric quantity coefficients for each type of users, so that the optimization of the daily load curves of residents is realized; in the inner-layer method, a user responds to the dispatching of the power grid according to the power package, so that the interaction degree of the power utilization end and the power supply end is improved.
The inner-layer optimization method realizes real-time monitoring and scheduling of the load by using a non-intrusive means. After the electric power package containing the peak-valley coefficient and the discount coefficient designed by the outer layer optimization method is transmitted to the inner layer, the inner layer model realizes intelligent management of household appliances based on the electric power package, the genetic optimization algorithm is adopted to simulate the day-ahead power utilization, realize non-invasive real-time monitoring of electric equipment and coping with the unplanned power utilization behavior, and adjust the power utilization plan in real time, so that the daily load curve of a user meets the peak-valley coefficient in the electric power package. The inner layer optimization method needs to use non-invasive load monitoring to realize the scheduling of the intelligent household appliances. Real-time power utilization information is obtained through a non-intrusive load monitoring and decomposing method, active power is selected as a load mark, and load decomposition is carried out by combining operation period information. In the intelligent home optimization scheduling, the real-time load monitoring can be realized by using a non-invasive mode, and the power utilization condition of a user is received in time, so that the limitation of the load prediction in the day ahead is improved. At present, non-invasive load identification algorithms are mainly divided into two types: one type of the system performs switching judgment or basic transform domain analysis according to time domain transient and steady state information, depends on passing electric equipment switching information in the short term, and has larger separation difficulty when various loads are mixed. The other type of intelligent identification algorithm based on pattern classification has high intelligent degree and flexible identification criteria, but has high algorithm complexity, difficult hardware realization and larger influence on identification accuracy by load quantity change.
According to the real-time decomposition condition of the intelligent household appliance, the dynamic priority of the household appliance is calculated, the priority of the equipment is described by the comfort degree of the equipment, the comfort degree index is larger, the satisfaction degree of a user is lower, and the electricity utilization priority of the user is higher. Schedulable devices are exemplified by water heaters and air conditioners, and are modeled to show their dynamic priorities. When the peak-valley electric quantity coefficient exceeds a standard value, the non-invasive load monitoring is used for obtaining the real-time equipment operation condition, and the household appliance with the lowest equipment priority is turned off preferentially or part of power of the adjustable equipment is reduced.
The electric power package required by the inner-layer optimization method is designed by the outer-layer optimization method, the outer-layer model analyzes the electricity utilization condition of a user by adopting an FCM fuzzy clustering algorithm under the environment of time-of-use electricity price, the daily load curve peak load shifting is taken as a target, and the genetic optimization algorithm is adopted to design the electric power package containing the excitation subsidy and the peak-valley coefficient. The time-of-use electricity price encourages users to reasonably arrange electricity utilization time and carry out peak clipping and valley filling, but in most of the existing researches, the time-of-use electricity price is only fixed electricity price, and the time-of-use electricity price mechanism cannot reflect the interaction relation between a power grid and the users and is not enough to mobilize the enthusiasm of resident users for participating in power grid peak clipping. Therefore, related departments and experts and scholars provide an incentive mechanism for promoting resident users to participate in power grid peak shaving, and certain subsidies are provided when residents reduce power consumption requirements.
In the invention, in order to scientifically and reasonably formulate an electric power package, guide the electricity consumption of residents and actively respond to time-of-use electricity price and incentive mechanisms, the daily load curve needs to be subjected to cluster analysis, and the classification analysis can be carried out on different types of resident load curves.
In order to reflect the similarity among loads, improve the operation efficiency and reduce the storage space, a daily load curve clustering method based on characteristic index dimensionality reduction and fuzzy clustering is adopted. And (3) performing feature dimensionality reduction on the time-series load curve by using the daily load feature index with clear physical significance, and classifying the daily load curve by using the Euclidean distance as a similarity basis. The excitation mechanism of the method is established on the basis of time-of-use electricity price, so that 4 angles of the whole day, the peak period, the valley period and the plateau period are considered when selecting the daily load characteristic index. The method selects 6 characteristic indexes as the basis of data dimension reduction. After the dimension reduction of the data is completed, the algorithm takes the characteristic dimension reduction matrix Y as input and the Euclidean distance as the similarity basis for clustering.
The Fuzzy C-means clustering Algorithm (FCM) is a partition-based Algorithm that ultimately achieves the highest possible similarity between samples classified into the same class, and the lowest possible similarity between samples. FCM is improved on the basis of common C-means clustering, and the concept of a fuzzy set is introduced, so that flexible fuzzy clustering division is realized. The fuzzy clustering describes the possibility of belonging to a certain class or the similarity of the same class, and breaks the limitation of hard division of 'non-0, namely 1'.
The optimal power package oriented to demand response is designed for each type of resident users obtained through FCM clustering analysis, and when the daily load of the users is smaller than a certain peak-valley difference, the electricity price is discounted, so that the effects of peak clipping and valley filling are achieved. The benefits of the power generation side, the power grid side and the environment side and the expenditure of the user electricity fee are comprehensively considered, the peak-valley electricity quantity coefficient standard value and the discount coefficient are used as decision variables, the optimal power package facing the demand response is obtained by optimizing for each type of resident users, when the daily load of the users is smaller than a certain peak-valley difference, the electricity price is discounted, and the effects of peak clipping and valley filling are achieved. The actual implementation effect of the power package needs to be specifically evaluated through the user benefits, namely, the reduced electric charge expenditure and the overall benefits of the three sides of power generation, power grid and environment.
Has the advantages that: compared with the prior art, the method and the system have the advantages that the randomness of the electricity utilization behavior of the user is considered, the traditional intrusive mode is replaced by a non-intrusive mode, the running condition of the household equipment is monitored in real time, and the electricity utilization plan is adjusted in time when the standard value of the peak-valley electric quantity coefficient is not met. The double-layer optimization model can realize the correction of the daily load curve of the user and the scheduling of specific intelligent household appliances, can visually analyze the influence of the optimized scheduling of the single-user intelligent household appliances on the peak clipping and valley filling of the whole resident load, replaces the current research mode of splitting the power utilization plan of a power supply end and the actual scheduling of the power utilization end, realizes the scheduling of the power package which takes time-sharing power price and incentive subsidy mechanism into account to specific load equipment of the household user, is used for improving the peak-valley difference of the resident load curve, improves the interaction degree of the power utilization end and the power supply end, and enables the resident user to more actively participate in the optimized scheduling of a power grid.
Drawings
FIG. 1 is a flow chart of a home energy bi-level optimization method of the present invention;
FIG. 2 is a schematic diagram of characteristic indicators of a daily load curve of simulation analysis according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of time-of-use electricity price information of simulation analysis according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating power consumption information of a schedulable device of a simulation analysis according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a clustering result of a daily load curve of simulation analysis according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a clustered daily load curve of a simulation analysis in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of a power package design for simulation analysis according to an embodiment of the present invention;
FIG. 8 is a graph of the daily load of a simulation analysis simulation in accordance with an embodiment of the present invention;
fig. 9 is a diagram of a non-intrusive load decomposition-based operation of a home appliance according to a simulation analysis of the embodiment of the present invention;
fig. 10 is a diagram of an unplanned appliance operating condition of simulation analysis according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The invention provides a family energy double-layer optimization method for realizing interaction between a power grid side and a user side, and the overall design principle, the method flow and the simulation analysis of the scheme are respectively explained below.
1. Double-layer optimization method for household energy
The home energy two-layer optimization algorithm comprises outer layer and inner layer optimization. On the basis of carrying out cluster analysis on daily load curves, the outer layer model designs an electric power package containing excitation subsidies and peak-valley electric quantity coefficients for each type of users, and optimization of the daily load curves of residents is achieved; and the user responds to the dispatching of the power grid according to the power package in the inner-layer model, so that the interaction degree of the power utilization end and the power supply end is improved. Meanwhile, the randomness of the electricity utilization behavior of the user is considered, the non-intrusive means is adopted to replace the traditional intrusive means, the running condition of the household equipment is monitored in real time, and the electricity utilization plan is adjusted in time when the standard value of the peak-valley electric quantity coefficient is not met.
1.1 outer layer optimization method-electric power package for designing and optimizing daily load curve
The outer-layer optimization method is characterized in that under the time-of-use electricity price environment, the FCM fuzzy clustering algorithm is adopted to analyze the electricity utilization condition of a user, the daily load curve peak clipping and valley filling is taken as a target, and the genetic optimization algorithm is adopted to design an electric power package containing an excitation subsidy and a peak-valley coefficient.
In this embodiment, the effective daily load curves of 298 users actually measured on a certain working day of 8 months in 2020 and 8 months in China are selected as research objects, and are collected every 60min, so that 24 measurement points are counted. And (3) selecting 6 characteristic indexes by adopting a fuzzy c-means clustering algorithm, and performing dimensionality reduction on the data. And determining the optimal clustering number by combining the possible class number of the daily load curve and the Silhouuette index.
Establishing a clustering objective function of FCM clustering analysis, namely:
Figure BDA0002758162420000101
in the formula: u is a fuzzy partition matrix; p is a clustering center matrix; p is a radical ofi＝(yi1,yi2,…,yim)T；g∈[0,2]Is the fuzzification degree coefficient; dij＝||yj-piI is a sample yjAnd the cluster center piThe euclidean distance of (c).
Establishing an optimization model of the electric power package design of the residential users, namely:
Figure BDA0002758162420000102
1.2 inner layer optimization method-real-time scheduling of intelligent household appliances according to dynamic priority of household appliances
The inner layer model realizes intelligent management of household appliances based on the electric power package, simulates day-ahead power utilization by adopting a genetic optimization algorithm, realizes non-invasive real-time monitoring and response of electric equipment to unplanned power utilization behaviors, and adjusts a power utilization plan in real time, so that a daily load curve of a user meets a peak-valley coefficient in the electric power package.
In this embodiment, a typical power usage load curve for category 1 users is selected for analysis. The parameter of the water heater and the air conditioner is TenAt 15 DEG, M is 80L, RhIs 0.7623, ChIs 431.7012, QhIs 150; raIs 5.56, CaIs 0.18, QaIs 2.5. Selecting Delta TWHIs 5 DEG, TWH,sIs 60 degrees; t isoutIs 35 DEG, TAC,sIs 25 DEG, Delta TACIs 3 deg.. Setting a demand response event specified period, which requires further adjustment of the unplanned electricity usage during the peak period in order to regulate the peak-to-valley charge factor value, so the demand response event specified period is set to 19: 00-22: 00. on the basis of the load curve of the last festival, users want to increase the use of a water heater in the peak period to meet the demand of hot water and increase the use of an air conditioner to adjust the indoor temperature to a comfortable level, and meanwhile, the peak-to-valley electric quantity coefficient of the users is guaranteed to be met.
2. Method flow
As shown in fig. 1, a double-layer power utilization optimization scheduling strategy is formulated in the environment of time-of-use power rates and incentive mechanisms. The left half part of the flow chart is used for realizing outer layer optimization design of the electric power package, package parameters of design, namely a peak-valley electric quantity coefficient and a discount coefficient, are input into the inner layer optimization of the right half part, and the inner layer optimization is carried out by taking the peak-valley coefficient standard value in the selected package as a constraint condition.
The steps of optimizing the scheduling strategy are as follows:
s1: reading a daily load curve of a user, reducing the dimension of high-dimensional load data based on the characteristic indexes, and acquiring a dimension reduction curve;
s2: calculating the membership degree of the dimensionality reduction curve based on an FCM fuzzy clustering algorithm, and finishing the classification of the daily load curve according to the membership degree;
s3: classifying the residential users according to the classification result of the daily load curve, and designing an electric power package for each type of residential users to obtain a peak-valley electric quantity coefficient standard value and an electric price discount coefficient which meet the best benefit of a power generation side, a power grid side and an environment side and the minimum electric charge expense of the users;
the effect of the discount coefficient is: when the load curve of the user meets the standard value of the peak-valley electric quantity coefficient, a certain electric price discount is given to the user as a subsidy, and the discount is made on the basis of the electric charge expenditure before the electric power package is adopted. That is, the comfort of the user is slightly lowered, and the electricity charge of the user is also reduced.
The power package designed on the outer layer comprises a peak-valley electric quantity coefficient and a power price discount coefficient, and when the inner layer is optimized, the peak-valley electric quantity coefficient is used as a constraint condition to constrain the scheduling of the intelligent household appliances.
S4: simulating a load curve before the day according to the peak-valley electric quantity coefficient, and carrying out real-time monitoring and load decomposition on the user household appliance by using a non-invasive optimization model;
s5: judging whether an unplanned power utilization behavior occurs, if not, executing power utilization according to an original plan, namely a load simulation curve before the day; if yes, calculating a peak-valley electric quantity coefficient containing the unplanned power consumption, judging whether the peak-valley electric quantity coefficient exceeds a standard value, if not, executing the power consumption according to the original plan, if so, dynamically adjusting the peak-valley electric quantity coefficient according to the dynamic priority of the user household appliance until the peak-valley electric quantity coefficient does not exceed the standard value; and giving the corresponding electricity price discount coefficient to the user when the peak-valley electricity quantity coefficient standard value is met.
In this embodiment, the method for calculating the membership degree of the dimensionality reduction curve in step S2 includes:
suppose the data set to be clustered is X ═ X1,x2,...,xnDimension reduction is carried out according to the characteristic indexes to obtain a data set Y ═ Y1,y2,...,ynIn which y isj＝(yj1,yj2,...,yj6)TEach sample is correspondingly provided with 6 characteristic attributes as a characteristic attribute vector; y iskjAs feature vector ykThe j-th dimension of the feature quantity; the FCM algorithm represents the clustering result by using a fuzzy partition matrix, namely
Figure BDA0002758162420000111
Wherein c is the number of quasi-classes; u. ofijThe j (j is 1,2, …, n) th data sample belongs to the membership degree of the i (i is 1,2, …, c) th class, and the following conditions are satisfied: to pair
Figure BDA0002758162420000112
To pair
Figure BDA0002758162420000113
Figure BDA0002758162420000114
In the embodiment, in step S2, a Silhouette index is used to evaluate the clustering quality, and the optimal clustering number of the daily load curve of the residents is determined; the Silhouette index is defined as follows:
given a total number of samples n, a number of pseudo-classifications
Figure BDA0002758162420000115
For sample j in class i, define di(j) Representing the in-class compactness degree as the average distance between the sample j and all other samples in the class, wherein the smaller the value of the in-class compactness degree is, the more compact the in-class compactness is; (ii) a Definition of do(j) And representing the dispersion degree among classes for the average distance from the sample j to all the samples which are not in the same class, wherein the larger the value of the dispersion degree among the classes is, the more the dispersion among the classes is. Silhouette index J of sample JSil(j) Expressed as:
Figure BDA0002758162420000116
the Silhouuette index mean J of all samplesSilmeanExpressed as:
Figure BDA0002758162420000121
JSilmeanthe number of classes corresponding to the maximum value is the optimal clustering number.
In this embodiment, the method for designing the power package in step S3 includes: establishing a power package model aiming at daily load, and optimizing the power package of the residential user by adopting a genetic algorithm, which specifically comprises the following steps:
the electricity fee expenditure is expressed as:
Figure BDA0002758162420000122
in the formula:
Figure BDA0002758162420000123
and
Figure BDA0002758162420000124
respectively the daily initial total electric charge of the class II user group and the total electric charge after the electric power set is carried out; bjA discount coefficient for electricity price; f. oftElectricity prices for a period of t; etaij,tThe proportion of the electricity consumption in the time period t of each day to the total daily electricity consumption after a package j is selected for a typical user i; p is a radical ofijProbability of choosing package j for a fully rational representative user i under ideal conditions;
Figure BDA0002758162420000125
the total daily electricity consumption of the ith user group is obtained;
wherein:
Figure BDA0002758162420000126
in the formula: u shapeijThe satisfaction degree of the user after the set of meal is used is shown, the satisfaction degree comprises the satisfaction degree of electricity expense and the satisfaction degree of an electricity consumption mode measured by daily load curve offset, and the satisfaction degree is specifically as follows:
Figure BDA0002758162420000127
in the formula: u shapeij,1Selecting the electricity expense satisfaction degree of the package j for the typical user i;
Figure BDA0002758162420000128
in the formula: u shapeij,2Selecting the electricity utilization satisfaction degree of a package j for a typical user i; etai,t' is the initial power usage proportion of a typical user i in a period t per day; r isiAnd q isiIs a parameter related to the satisfaction degree of the electricity using mode;
Uij＝αiUij,1+(1-αi)Uij,2 αi∈[0,1] (8)
in the formula: alpha is alphaiThe weight given to the satisfaction degree of the typical user on the electricity expense is represented, and the sensitivity degree of different types of users on the reduction of the electricity expense and the change of the electricity utilization mode is reflected;
the electric power selling income reduced after the electric power package is pushed out by the power grid side, namely the electric power expense reduced by the user, and the cost for pushing out the electric power package by the power grid side comprises the marketing and management expense C of the electric power packaget；
The power generation side benefits include that the cost of newly increased power generation capacity can be avoided, the power generation cost of a high-cost unit can be avoided, and the abnormal start-stop cost of the unit can be avoided, and are expressed as follows:
Figure BDA0002758162420000131
in the formula: a. theTThe cost per kilowatt capacity can be avoided; a. thegThe unit power generation cost of a high-cost unit; a. thesThe cost is the abnormal starting and stopping cost each time; n issThe number of abnormal starting and stopping times is reduced; k is a radical of1As a percentage of planned reserve capacity; k is a radical of2Loss coefficients of the transmission and distribution network; k is a radical of3The plant power rate; the delta P is the potential peak load reduction of the power package; the delta Q is the total electricity consumption of the load of residents in the peak period for implementing the reduction of the electric power combo;
the benefits of the power grid side include the avoidable power grid investment cost, the system reliability benefits, and the avoidable electricity cost, which are expressed as:
Figure BDA0002758162420000132
in the formula: a. thepThe unit average cost of the transmission line, the transformer substation and the supporting facilities thereof is calculated; a. thefA difference in the cost of electricity purchased for peak and valley charges per kilowatt-hour; a. theVOLLThe value of power loss load; a. theSMPIs the marginal cost of electricity; p is a radical ofLOLPThe power system load loss probability;
environmental benefit refers to value resulting from avoiding greenhouse gas emission reduction due to thermal power generation. After the electric power set is implemented, on one hand, the power generation side power generation amount mainly based on thermal power generation is reduced, and on the other hand, the isothermal chamber gas emission amount is reduced due to the improvement of the load factor and the reduction of the number of times of starting and stopping the generator set. In summary, the environmental benefit is expressed as:
Figure BDA0002758162420000133
in the formula: qAAnd Δ QARespectively the total power generation amount and the reduced power generation amount after the package is implemented;
Figure BDA0002758162420000134
and
Figure BDA0002758162420000135
the emission reduction coefficients and the emission reduction values of carbon dioxide, sulfur dioxide and nitric oxide are respectively; delta xi is the percentage point of the improvement of the postprandial load rate of the electric power set; c. CgThe coal consumption is supplied to the coal-fired unit;
Figure BDA0002758162420000136
the load rate is the unit coal consumption reduction rate of the coal-fired unit corresponding to 1 percent point of the load rate increase; for simplification of the expression, use rΣRepresenting the total value of greenhouse gas emission reduction per reduction of unit power generation.
The optimization model of the electric power package design of the residential users is shown as the following formula:
Figure BDA0002758162420000137
in step S5, the dynamic priority model is used to obtain the dynamic priority of the user' S home appliance, where the dynamic priority model includes a water heater model and an air conditioner model, and the method specifically includes:
the water heater model:
when the water heater is in an opening state, a closing state and a water using state in a time interval, the water temperature at the moment is respectively shown as formulas (13) to (15), and the constraint condition is shown as formula (16):
Figure BDA0002758162420000141
Figure BDA0002758162420000142
TWH,t＝[TWH,t-1·(M-dn+1)+Ten·dn+1]/M (15)
TWH,t∈[TWH,s-ΔTWH,TWH,s] (16)
in the formula: t isenRepresents the ambient temperature, i.e., the cold water temperature; τ represents a time interval; m represents the capacity of the water heater; rhRepresenting the thermal resistance coefficient of the water heater; chRepresenting the heat capacity of the water heater; qhRepresenting the water heater power; dn+1Is shown during a time period t-1, t]The amount of cold water added internally; t isWH,sRepresenting a maximum water temperature set value; delta TWHSetting a range for the water temperature;
the water heater operating state is related to the water temperature setting, when the water temperature is higher than the maximum temperature TWH,sWhen the water heater is powered off; when the temperature is lower than the lowest temperature, the water heater is electrified; when the water heater is in the set range, the water heater keeps the original state; water heater control model and comfort index K thereofWH,tThe following were used:
Figure BDA0002758162420000143
Figure BDA0002758162420000144
in the formula: sWH,tThe working state of the water heater is in a t period (the value is 0 to indicate power failure, and the value is 1 to indicate power on); t isWH,tWater temperature is t time period;
KWH,tis the difference between the current water temperature and the highest set value after per unit, the higher the water temperature is, the comfort index KWH,tThe larger the user is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And during the demand response period, controlling the power-on and power-off states of the water heaters according to the priority of the water heaters.
An air-conditioning model:
when the air conditioner is in [ t-1, t ]]Room temperature T at time T in the on and off statesAC,tThe running constraint conditions are shown as formulas (19) and (20) and as formula (21);
Figure BDA0002758162420000145
Figure BDA0002758162420000146
T∈[TAC,s,TAC,s+ΔTAC] (21)
in the formula: raRepresenting the thermal resistance coefficient of the air conditioner; caRepresenting air conditioner heat capacity; qaRepresenting air conditioner power; t isoutRepresents the ambient temperature; t isAC,sRepresents the lowest room temperature set value; delta TACSetting a range for room temperature;
assuming that the air conditioner works in a refrigeration mode, the running state of the air conditioner is related to the room temperature setting, and when the room temperature is higher than the maximum value, the air conditioner is electrified; when the air temperature is lower than the minimum value, the air conditioner is powered off; when the air conditioner is in the set range, the air conditioner keeps the original state; control model and comfort index K thereofAPPThe calculation is as follows:
Figure BDA0002758162420000151
Figure BDA0002758162420000152
in the formula, SAC,tThe working state of the air conditioner is t time (the value is 0 to indicate power-off, and the value is 1 to indicate power-on); t isAC,tRoom temperature for a period of t;
KAC,tis the difference between the current room temperature and the lowest set value after per unit, the higher the room temperature is, the comfort index KAC,tThe larger the user is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And during the demand response period, controlling the power-on and power-off states of the air conditioners according to the priorities of the air conditioners.
After the current peak-to-valley electricity quantity coefficient does not exceed the standard value in step S5 of the present embodiment, the electrical appliances for which the plan is to be changed need to be counted, and the electricity utilization plans of the electrical appliances are to be re-planned.
3. Simulation analysis
Based on the above scheme, the simulation analysis of this embodiment selects the effective daily load curves of 298 users actually measured on a certain working day of 8 months in 2020 and China, which are the research objects.
3.1 clustering condition of daily load curve of residents
Selecting a daily load curve of a certain city, collecting every 60min, and counting 24 measuring points in total. The market is supposed to adopt time-of-use electricity prices, and the division of the time-of-use electricity prices is shown in fig. 3.
And (3) selecting 6 characteristic indexes by adopting a fuzzy c-means clustering algorithm, wherein the 6 characteristic indexes are specifically shown in figure 2, and performing dimensionality reduction on the data. And determining the optimal clustering number by combining the possible clustering number of the daily load curve and the Silhouette index, simulating the clustering number of 2-6 by combining the actual condition, and as can be known from the following table, when the clustering number is 4, the value of the Silhouette index is the maximum, so that 4 is the optimal clustering number. As shown in fig. 5 and fig. 6, the clustering results are classified into four categories, namely category 1(70 items), category 2(88 items), category 3(44 items), and category 4(96 items), as can be seen from fig. 5, and (a), (b), (c), and (d) in fig. 5 are schematic diagrams of clustering of these four categories, respectively, and fig. 6 is a daily load graph of four categories of user groups.
Figure BDA0002758162420000161
3.2 design of Power packages
The optimization model of the method belongs to the complex nonlinear programming problem with constraints, and is difficult to solve by the traditional analytic method, so that the genetic algorithm is adopted for optimization solution in the research of the method. The optimal power package plan obtained by the 4 different types of user groups is shown in fig. 7.
The difference of the electricity utilization habits of the residential users leads to the difference of the peak-valley time of the daytime load. The shapes of daily load curves are different, so that the peak-to-valley electric quantity coefficients are greatly different. Therefore, 4 kinds of electric power packages are designed according to the result of the cluster analysis, and the power utilization habits of different users are met.
When the peak-valley electric quantity coefficient standard value in the package selected by the user is close to the peak-valley electric quantity coefficient of the user only according to the habit of the user, the requirement of the package can be met only by slightly changing the power utilization time of individual equipment and staggering the peak power utilization, the corresponding power price discount coefficient is obtained, and the power utilization cost is saved.
Taking a typical daily load curve as an example, if 4 types of residential users respectively adopt the 4 types of packages, the total daily load curve of the users tends to be smooth, the peak-valley difference is reduced, and the power generation cost of the power grid is reduced.
3.3 simulation of daily load curve and real-time running condition of household appliance
The method takes the day-ahead load curve as an original power utilization plan, carries out non-invasive load monitoring on the basis, and readjusts the power utilization plan when the unplanned power utilization occurs.
The load curve before the day is simulated according to the optimal power package simulated by the outer layer model, and taking a certain family of the class 1 as an example, the power consumption information of schedulable device of the family is shown in fig. 4.
And according to the operable time, the minimum operating time and the interruption possibility of the schedulable device in the table, taking the peak-valley electric quantity coefficient of the electric power package 1 as a target function, performing optimized scheduling on the schedulable device to enable the load curve to meet the peak-valley difference, and finding an optimal solution by adopting a genetic optimization algorithm. The optimal load scheduling case in this embodiment is shown in fig. 8.
If no unplanned power utilization occurs, the load is decomposed by the daily load curve of fig. 3 and the schedulable device operable time and power, and the result of non-intrusive real-time load monitoring can be obtained, as shown in fig. 9.
3.4 Intelligent household appliance control simulation analysis based on dynamic priority
The method selects a typical electricity load curve of the class 1 user for analysis. The current peak-to-valley electricity coefficient is 2.861, if the user temporarily changes the electricity plan, when the peak-to-valley electricity coefficient exceeds the standard value, if the water heater and the air conditioner are running, the dynamic priority of the water heater and the air conditioner is checked, and whether the water heater and the air conditioner can be turned off to meet the standard value is checked. As can be seen from the simulation of the water heater in fig. 10, if at 19: 00-21: 00 required the use of hot water and 20:00 to 21:00 required the turning on of the water heater at night to maintain a comfortable temperature. If desired, at 19: 00-21: 00 to maintain the room at the temperature range set by the user, the air conditioner needs to be turned on intermittently for this period of time. And calculating by combining the daily load of the user in the previous section, and if the water heater is used alone for one hour, the peak-to-valley electric quantity coefficient still meets the standard value. However, if the use of the air conditioner is increased, the peak-to-valley electric quantity coefficient may exceed the standard value, and therefore, if the requirement of the standard value is met, the household appliance cannot be turned on or off completely according to the requirement of the user, and the comfort of the user is affected to a certain extent. The conditions of scheduling the water heater and the air conditioner according to the dynamic priority if the peak-to-valley electric quantity coefficient standard value is required to be met are analyzed.
When considering only the dynamic priority of the home device, the comfort of the user is relatively reduced, as shown in fig. 7, at 20: after 30, the temperature of the water heater does not reach the minimum temperature of 55 ℃ required by a user, and the temperature of the air conditioner fluctuates around the upper limit of 28 ℃.
When the dynamic priority of the home appliance is considered and the peak-to-valley charge factor standard value is satisfied, the comfort of the user is significantly reduced, as shown in fig. 8, from 19: 00, the priority of the water heater is continuously lower than that of the air conditioner, the air conditioner is continuously operated, and at 19: 30-19: 45 water heaters only heat for a short time, after which the peak-to-valley charge factor will not meet the standard value if the water heater or air conditioner is turned on again. In this case, at 20: and after 00, the temperature of the water heater does not reach the minimum temperature 55 ℃ required by a user, and the temperature of the air conditioner is controlled in a range of 19: 45 already exceeds the upper limit of 28 c.

Claims (6)

1. A family energy double-layer optimization method for realizing interaction between a power grid side and a user side is characterized by comprising the following steps: the method comprises the following steps:
s1: reading a daily load curve of a user, reducing the dimension of high-dimensional load data based on the characteristic indexes, and acquiring a dimension reduction curve;
s2: calculating the membership degree of the dimensionality reduction curve based on an FCM fuzzy clustering algorithm, and finishing the classification of the daily load curve according to the membership degree;
s3: classifying the residential users according to the classification result of the daily load curve, and designing an electric power package for each type of residential users to obtain a peak-valley electric quantity coefficient standard value and an electric price discount coefficient which meet the best benefit of a power generation side, a power grid side and an environment side and the minimum electric charge expense of the users;
s4: simulating a load curve before the day according to the peak-valley electric quantity coefficient, and carrying out real-time monitoring and load decomposition on the user household appliance by using a non-invasive optimization model;
s5: judging whether an unplanned power utilization behavior occurs, if not, executing power utilization according to an original plan, namely a load simulation curve before the day; if yes, calculating a peak-valley electric quantity coefficient containing the unplanned power consumption, judging whether the peak-valley electric quantity coefficient exceeds a standard value, if not, executing the power consumption according to the original plan, if so, dynamically adjusting the peak-valley electric quantity coefficient according to the dynamic priority of the user household appliance until the peak-valley electric quantity coefficient does not exceed the standard value; and giving the corresponding electricity price discount coefficient to the user when the peak-valley electricity quantity coefficient standard value is met.
2. The method of claim 1 for implementing a dual-layer optimization of energy in a home with grid-side and user-side interaction, wherein: the method for calculating the membership degree of the dimensionality reduction curve in the step S2 comprises the following steps:
suppose the data set to be clustered is X ═ X1,x2,...,xnDimension reduction is carried out according to the characteristic indexes to obtain a data set Y ═ Y1,y2,...,ynIn which y isj＝(yj1,yj2,...,yj6)TEach sample is correspondingly provided with 6 characteristic attributes as a characteristic attribute vector; y iskjAs feature vector ykThe j-th dimension of the feature quantity; the FCM algorithm represents the clustering result by using a fuzzy partition matrix, namely
Figure FDA0002758162410000011
Wherein c is the number of quasi-classes; u. ofijThe j (j is 1,2, …, n) th data sample belongs to the membership degree of the i (i is 1,2, …, c) th class, and the following conditions are satisfied: to pair
Figure FDA0002758162410000012
To pair
Figure FDA0002758162410000013
Wherein
Figure FDA0002758162410000014
Representing any i and any j.
3. The method of claim 2 for implementing a dual-layer optimization of energy in a home with grid-side and user-side interaction, wherein: in the step S2, a Silhouette index is adopted to evaluate the clustering quality and determine the optimal clustering number of the daily load curve of the residents; the Silhouette index is defined as follows:
given a total number of samples n, a pseudo-classification number c, for a sample j in the ith class, d is definedi(j) Representing the in-class closeness degree for the average distance between the sample j and all other samples in the class; definition of do(j) Is the average distance of sample j to all samples that are not homogeneousCharacterization of degree of dispersion between classes, Silhouette index J of sample JSil(j) Expressed as:
Figure FDA0002758162410000021
the Silhouuette index mean J of all samplesSilmeanExpressed as:
Figure FDA0002758162410000022
JSilmeanthe number of classes corresponding to the maximum value is the optimal clustering number.
4. The method of claim 1 for implementing a dual-layer optimization of energy in a home with grid-side and user-side interaction, wherein: the design method of the power package in the step S3 is as follows: establishing an electric power package model aiming at daily load, wherein the electric power package comprises a peak-valley electric quantity coefficient standard value and an electric price discount coefficient, and optimizing the electric power package of a resident user by adopting a genetic algorithm, specifically comprising the following steps:
the electricity fee expenditure is expressed as:
Figure FDA0002758162410000023
in the formula:
Figure FDA0002758162410000024
and
Figure FDA0002758162410000025
respectively the daily initial total electric charge of the class II user group and the total electric charge after the electric power set is carried out; bjA discount coefficient for electricity price; f. oftElectricity prices for a period of t; etaij,tThe proportion of the electricity consumption in the time period t of each day to the total daily electricity consumption after a package j is selected for a typical user i; p is a radical ofijIs ideally completeProbability of a rational representative user i selecting package j;
Figure FDA0002758162410000026
the total daily electricity consumption of the ith user group is obtained;
wherein:
Figure FDA0002758162410000027
in the formula: u shapeijThe satisfaction degree of the user after the set of meal is used is shown, the satisfaction degree comprises the satisfaction degree of electricity expense and the satisfaction degree of an electricity consumption mode measured by daily load curve offset, and the satisfaction degree is specifically as follows:
Figure FDA0002758162410000028
in the formula: u shapeij,1Selecting the electricity expense satisfaction degree of the package j for the typical user i;
Figure FDA0002758162410000029
in the formula: u shapeij,2Selecting the electricity utilization satisfaction degree of a package j for a typical user i; etai,t' is the initial power usage proportion of a typical user i in a period t per day; r isiAnd q isiIs a parameter related to the satisfaction degree of the electricity using mode;
Uij＝αiUij,1+(1-αi)Uij,2 αi∈[0,1] (8)
in the formula: alpha is alphaiRepresents the weight given to the satisfaction degree of the typical user' on the electricity fee expenditure;
the electric power selling income reduced after the electric power package is pushed out by the power grid side, namely the electric power expense reduced by the user, and the cost for pushing out the electric power package by the power grid side comprises the marketing and management expense C of the electric power packaget；
The power generation side benefits include that the cost of newly increased power generation capacity can be avoided, the power generation cost of a high-cost unit can be avoided, and the abnormal start-stop cost of the unit can be avoided, and are expressed as follows:
Figure FDA0002758162410000031
in the formula: a. theTThe cost per kilowatt capacity can be avoided; a. thegThe unit power generation cost of a high-cost unit; a. thesThe cost is the abnormal starting and stopping cost each time; n issThe number of abnormal starting and stopping times is reduced; k is a radical of1As a percentage of planned reserve capacity; k is a radical of2Loss coefficients of the transmission and distribution network; k is a radical of3The plant power rate; the delta P is the potential peak load reduction of the power package; the delta Q is the total electricity consumption of the load of residents in the peak period for implementing the reduction of the electric power combo;
the benefits of the power grid side include the avoidable power grid investment cost, the system reliability benefits, and the avoidable electricity cost, which are expressed as:
Figure FDA0002758162410000032
in the formula: a. thepThe unit average cost of the transmission line, the transformer substation and the supporting facilities thereof is calculated; a. thefA difference in the cost of electricity purchased for peak and valley charges per kilowatt-hour; a. theVOLLThe value of power loss load; a. theSMPIs the marginal cost of electricity; p is a radical ofLOLPThe power system load loss probability;
the environmental benefit is expressed as:
Figure FDA0002758162410000033
in the formula: qAAnd Δ QARespectively the total power generation amount and the reduced power generation amount after the package is implemented;
Figure FDA0002758162410000034
and
Figure FDA0002758162410000035
the emission reduction coefficients and the emission reduction values of carbon dioxide, sulfur dioxide and nitric oxide are respectively; delta xi is the percentage point of the improvement of the postprandial load rate of the electric power set; c. CgThe coal consumption is supplied to the coal-fired unit;
Figure FDA0002758162410000036
the load rate is the unit coal consumption reduction rate of the coal-fired unit corresponding to 1 percent point of the load rate increase; for simplification of the expression, use rΣRepresents the total value of greenhouse gas emission reduction per reduction of unit power generation;
in summary, the optimization model of the electric power package design of the residential users is shown as follows:
Figure FDA0002758162410000041
5. the method of claim 4, wherein the method comprises the following steps: the method for obtaining the peak-to-valley electric quantity coefficient standard value and the discount coefficient of the excitation mechanism in the step S3 includes: and selecting a typical daily load curve in each type of residential users as a research object, and satisfying the optimization model by adopting a genetic algorithm to obtain an individualized power package.
6. The method of claim 1 for implementing a dual-layer optimization of energy in a home with grid-side and user-side interaction, wherein: in step S5, a dynamic priority model is used to obtain the dynamic priority of the user' S appliance, where the dynamic priority model includes a water heater model and an air conditioner model, and the method specifically includes:
the water heater model:
when the water heater is in an opening state, a closing state and a water using state in a time interval, the water temperature at the moment is respectively shown as formulas (13) to (15), and the constraint condition is shown as formula (16):
Figure FDA0002758162410000042
Figure FDA0002758162410000043
TWH,t＝[TWH,t-1·(M-dn+1)+Ten·dn+1]/M (15)
TWH,t∈[TWH,s-ΔTWH,TWH,s] (16)
in the formula: t isenRepresents the ambient temperature, i.e., the cold water temperature; τ represents a time interval; m represents the capacity of the water heater; rhRepresenting the thermal resistance coefficient of the water heater; chRepresenting the heat capacity of the water heater; qhRepresenting the water heater power; dn+1Is shown during a time period t-1, t]The amount of cold water added internally; t isWH,sRepresenting a maximum water temperature set value; delta TWHSetting a range for the water temperature;
the water heater operating state is related to the water temperature setting, when the water temperature is higher than the maximum temperature TWH,sWhen the water heater is powered off; when the temperature is lower than the lowest temperature, the water heater is electrified; when the water heater is in the set range, the water heater keeps the original state; water heater control model and comfort index K thereofWH,tThe following were used:
Figure FDA0002758162410000044
Figure FDA0002758162410000045
in the formula: sWH,tThe working state of the water heater is t time period; t isWH,tWater temperature is t time period;
KWH,tis the difference between the current water temperature and the highest set value after per unit;
an air-conditioning model:
when the air conditioner is in [ t-1, t ]]Room temperature T at time T in the on and off statesAC,tThe running constraint conditions are shown as formulas (19) and (20) and as formula (21);
Figure FDA0002758162410000051
Figure FDA0002758162410000052
T∈[TAC,s,TAC,s+ΔTAC] (21)
in the formula: raRepresenting the thermal resistance coefficient of the air conditioner; caRepresenting air conditioner heat capacity; qaRepresenting air conditioner power; t isoutRepresents the ambient temperature; t isAC,sRepresents the lowest room temperature set value; delta TACSetting a range for room temperature;
assuming that the air conditioner works in a refrigeration mode, the running state of the air conditioner is related to the room temperature setting, and when the room temperature is higher than the maximum value, the air conditioner is electrified; when the air temperature is lower than the minimum value, the air conditioner is powered off; when the air conditioner is in the set range, the air conditioner keeps the original state; control model and comfort index K thereofAPPThe calculation is as follows:
Figure FDA0002758162410000053
Figure FDA0002758162410000054
in the formula, SAC,tThe working state of the air conditioner is t time period; t isAC,tRoom temperature for a period of t;
KAC,tis the difference between the current room temperature and the lowest set value after per unit.
CN202011209070.XA 2020-11-03 2020-11-03 Household energy double-layer optimization method for realizing interaction between power grid side and user side Pending CN112366699A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011209070.XA CN112366699A (en) 2020-11-03 2020-11-03 Household energy double-layer optimization method for realizing interaction between power grid side and user side

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011209070.XA CN112366699A (en) 2020-11-03 2020-11-03 Household energy double-layer optimization method for realizing interaction between power grid side and user side

Publications (1)

Publication Number Publication Date
CN112366699A true CN112366699A (en) 2021-02-12

Family

ID=74513615

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011209070.XA Pending CN112366699A (en) 2020-11-03 2020-11-03 Household energy double-layer optimization method for realizing interaction between power grid side and user side

Country Status (1)

Country Link
CN (1) CN112366699A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113361831A (en) * 2021-08-10 2021-09-07 国网江苏省电力有限公司营销服务中心 Non-invasive load identification electric quantity decomposition method and system based on priority distribution
CN113572181A (en) * 2021-07-01 2021-10-29 湘潭大学 Electrified railway energy storage system double-layer capacity configuration optimization method based on time-of-use electricity price
CN113610426A (en) * 2021-08-19 2021-11-05 长沙理工大学 Intelligent electricity utilization community energy management method based on user satisfaction
CN113746089A (en) * 2021-08-31 2021-12-03 河海大学 Multi-user-oriented multi-time-scale power package and family energy optimization method
CN114200844A (en) * 2021-10-29 2022-03-18 国网江苏省电力有限公司 Intelligent household appliance control method based on dynamic priority ranking
CN115046246A (en) * 2022-08-15 2022-09-13 东方电子股份有限公司 Self-optimization-seeking control system for comprehensive energy
CN115171280A (en) * 2022-08-01 2022-10-11 宁波三星医疗电气股份有限公司 Fee control processing method of prepaid electric energy meter, intelligent electric meter and storage medium
CN116505595A (en) * 2023-06-29 2023-07-28 国网安徽省电力有限公司巢湖市供电公司 Power monitoring, dispatching and managing system based on virtual power plant

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101706561A (en) * 2009-11-20 2010-05-12 电子科技大学 Clustering method for functional magnetic resonance images
CN104850013A (en) * 2015-04-28 2015-08-19 南京邮电大学 Intelligent electricity utilization method of household appliances
CN109086922A (en) * 2018-07-19 2018-12-25 浙江大学 A kind of demand response electric power set meal optimum design method towards industry and commerce user
CN111092434A (en) * 2019-12-25 2020-05-01 天津大学 Residential community power load control method and device based on non-invasive electricity consumption data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101706561A (en) * 2009-11-20 2010-05-12 电子科技大学 Clustering method for functional magnetic resonance images
CN104850013A (en) * 2015-04-28 2015-08-19 南京邮电大学 Intelligent electricity utilization method of household appliances
CN109086922A (en) * 2018-07-19 2018-12-25 浙江大学 A kind of demand response electric power set meal optimum design method towards industry and commerce user
CN111092434A (en) * 2019-12-25 2020-05-01 天津大学 Residential community power load control method and device based on non-invasive electricity consumption data

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113572181A (en) * 2021-07-01 2021-10-29 湘潭大学 Electrified railway energy storage system double-layer capacity configuration optimization method based on time-of-use electricity price
CN113361831A (en) * 2021-08-10 2021-09-07 国网江苏省电力有限公司营销服务中心 Non-invasive load identification electric quantity decomposition method and system based on priority distribution
CN113610426A (en) * 2021-08-19 2021-11-05 长沙理工大学 Intelligent electricity utilization community energy management method based on user satisfaction
CN113610426B (en) * 2021-08-19 2024-03-29 长沙理工大学 Intelligent electricity community energy management method based on user satisfaction
CN113746089A (en) * 2021-08-31 2021-12-03 河海大学 Multi-user-oriented multi-time-scale power package and family energy optimization method
CN114200844B (en) * 2021-10-29 2024-01-09 国网江苏省电力有限公司 Intelligent household appliance control method based on dynamic priority ordering
CN114200844A (en) * 2021-10-29 2022-03-18 国网江苏省电力有限公司 Intelligent household appliance control method based on dynamic priority ranking
CN115171280A (en) * 2022-08-01 2022-10-11 宁波三星医疗电气股份有限公司 Fee control processing method of prepaid electric energy meter, intelligent electric meter and storage medium
CN115171280B (en) * 2022-08-01 2023-08-11 宁波三星医疗电气股份有限公司 Fee control processing method of prepaid electric energy meter, intelligent electric energy meter and storage medium
CN115046246B (en) * 2022-08-15 2022-11-15 东方电子股份有限公司 Self-optimization-seeking control system for comprehensive energy
CN115046246A (en) * 2022-08-15 2022-09-13 东方电子股份有限公司 Self-optimization-seeking control system for comprehensive energy
CN116505595A (en) * 2023-06-29 2023-07-28 国网安徽省电力有限公司巢湖市供电公司 Power monitoring, dispatching and managing system based on virtual power plant
CN116505595B (en) * 2023-06-29 2023-09-15 国网安徽省电力有限公司巢湖市供电公司 Power monitoring, dispatching and managing system based on virtual power plant

Similar Documents

Publication Publication Date Title
CN112366699A (en) Household energy double-layer optimization method for realizing interaction between power grid side and user side
Stavrakas et al. A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector
Javaid et al. Towards buildings energy management: using seasonal schedules under time of use pricing tariff via deep neuro-fuzzy optimizer
Spandagos et al. Fuzzy model of residential energy decision-making considering behavioral economic concepts
Chehreghani Bozchalui Optimal operation of energy hubs in the context of smart grids
CN114862252A (en) Load-adjustable multi-layer aggregation scheduling potential analysis method, system, equipment and medium
Liang et al. Economic MPC-based smart home scheduling with comprehensive load types, real-time tariffs, and intermittent DERs
Wang et al. Modeling of regional electrical heating load characteristics considering user response behavior difference
Jin et al. A deep neural network coordination model for electric heating and cooling loads based on IoT data
Alden et al. Artificial intelligence method for the forecast and separation of total and hvac loads with application to energy management of smart and nze homes
Wang et al. Unit scheduling considering the flexibility of intelligent temperature control appliances under TOU power price
Yu et al. Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models
Wang et al. Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units
Wang et al. Analysis of decision-making for air conditioning users based on the discrete choice model
Song et al. Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy
CN115115145B (en) Demand response scheduling method and system for distributed photovoltaic intelligent residence
CN115169839A (en) Heating load scheduling method based on data-physics-knowledge combined drive
Nadeem et al. Short term load forecasting on PRECON dataset
Wenhao et al. An analytical method for intelligent electricity use pattern with demand response
Jain et al. Clustering based short term load forecasting using artificial neural network
Chen et al. Optimization modeling method for coal-to-electricity heating load considering differential decisions
Chen et al. An Air Conditioning Control Method Based on Non-measurement Thermal Comfort Standard Correction for Demand Response
Chen et al. Review on Smart Meter Data Clustering and Demand Response Analytics
Ali et al. Combination of PSO and TS-F for Electricity Load Management Optimization: A Case Study of Poultry Sector
Keshtkar Development of an adaptive fuzzy logic system for energy management in residential buildings

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

Application publication date: 20210212