CN115018198A - Residential user electricity utilization optimization strategy considering differentiated demand response scheme - Google Patents
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Abstract
The invention belongs to the technical field of power demand side management, and particularly relates to a residential user power utilization optimization strategy considering a differentiated demand response scheme; s1, constructing a user data fuzzy set based on Wasserstein distance to accurately reflect user reference capacity; formulating an image based on occurrence probability characteristics and reference capacity of various modes of a user; s2, aiming at the problems of fuzziness of behaviors of residential users, limitation of long-term portrayal and the like, a demand response adaptation user optimization mechanism considering double time scales is constructed; s3, for price type preferred users, adopting a fuzzy clustering model to reconstruct the electricity price time period; s4, integrating the identification modes of price type and excitation type differential demand response schemes, and establishing a two-time-period source load interaction robust decision framework in the day-ahead and day; the electricity utilization optimization strategy for the residential users is provided, which improves the decision precision of demand response potential user optimization, improves the source load matching level, the user response willingness and the wind and light consumption level, and considers the differentiated demand response scheme.
Description
Technical Field
The invention belongs to the technical field of power demand side management, and particularly relates to a residential user power utilization optimization strategy considering a differentiated demand response scheme.
Background
With the rapid development of a novel power system and an energy internet technology, the scale of residential users is continuously increased, the gravity center transfer in the power industry is continuously promoted, the management of a single demand side is shifted to the active participation of flexible resources in source-load interaction of a user side, and the supply and demand balance of the power system is maintained; at the present stage, the power load data is characterized by mass, the availability of demand response is evaluated, and the method has important research value for improving the operation economy and robustness of a power grid; currently, research on demand response can be divided into an incentive type and a price type; the incentive type flexibly guides the time-varying power load of the user through subsidy, and the price type encourages the user to flexibly adjust through the time-varying price so as to obtain the maximum benefit; however, due to the fact that the users with different electricity consumption behavior portraits have different adaptation degrees to the two response strategies, if the two response strategies are implemented according to a traditional response scheme which does not show differentiation characteristics, the optimal benefit of all the users cannot be met at the same time; therefore, the method has high economic utilization value by accurately identifying the adaptation degree according to the power utilization mode and making a differentiated demand response strategy.
In the aspect of power consumption behavior portrayal, the prior art extracts a power consumption mode reflecting user characteristics through a questionnaire form, extracts a typical mode based on an integrated comprehensive clustering method of weighted voting from the aspects of load curve data and regulation potential, and explores the influence on the energy consumption habit and selection of resident users; however, the method has strong subjectivity and has great limitation in the aspect of data accuracy; in view of the above, in the current stage, the clustering-based residential user profile is mainly analyzed for the relevance of different power utilization indexes, and a power utilization mode and a behavior profile are mined by adopting a data analysis clustering algorithm; however, the current research focuses on analysis tag generation, and the multi-dimensional behavior tag is relatively simple, and lacks in-depth research on optimization and availability verification of a behavior portrait-based demand response scheme.
There is no clear definition mode in the aspect of analysis of the demand response scheme, and the evaluation of demand response availability is not researched from the aspect of behavior image classification, so that a refined demand response scheme is identified; the price type refers to that the user rearranges time intervals and electric quantity according to time interval division, the time-of-use electricity price can be better suitable for price type preferred users, and the effect of peak shifting and valley filling can be achieved; in the aspect of time interval division, the existing time interval division mode may no longer be applicable, the time interval boundary becomes fuzzy, and the renewable energy consumption rate may be affected, so that a more accurate time interval division mode needs to be formulated urgently; the incentive type means that a power company sets a compensation electricity price to attract users to participate in responding and carrying out electricity utilization time period transfer; aiming at an incentive type identification technology, due to the fact that daily electricity consumption behaviors of residential users are high in intermittence, when small-probability events are responded, a long-term scale portrait result has certain deviation on incentive type potential portrayal of the users; therefore, the method has great significance in providing an incentive type preferred user potential identification mechanism under the comprehensive consideration of the long-term and short-term scheduling scale.
The research of the electricity utilization behavior optimization method is still in a starting stage, the number of main bodies of demand response participation is small, and the marketization is not strong; meanwhile, in the aspect of translational load control, the research on the actual power utilization situation is not deep enough, the fitting degree of the actual energy utilization situation of a user is lacked, and an effective excitation effect and effective adjustment cannot be achieved; in addition, with the continuous promotion of the innovation of novel power systems, the source-load interaction capacity is widely concerned, the initiative of electricity optimization of residents is more obvious, and the requirement on high-proportion consumption of renewable energy sources is higher; in the prior art, the research on the user optimization strategy due to the intermittent uncertainty of the output of the renewable energy is lacked, random scenes are generated mostly in a random planning mode, and then the optimization strategy is formulated; however, the method is limited to the precise output probability distribution function, and the calculation complexity seriously affects the scheduling result.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a residential user electricity utilization optimization strategy which can accurately identify a high-potential target user, realize accurate portrait of incentive type demand response adaptation degree, improve the decision precision of demand response potential user optimization, and improve the source load matching level, the user response willingness and the wind and light consumption level, and considers a differential demand response scheme.
The purpose of the invention is realized as follows: a residential customer electricity optimization strategy considering a differential demand response scheme, comprising the steps of:
s1, aiming at the diversified trend of the power consumption behaviors of the user, constructing a characteristic analysis model of the daily load patterns of the user by extracting the common patterns of the user and analyzing the occurrence probability of the user on different daily load patterns, and representing the diversified power consumption behaviors; constructing a user data fuzzy set based on Wasserstein distance to accurately reflect user reference capacity; formulating an image based on occurrence probability characteristics and reference capacity of various modes of a user;
s2, aiming at the problems of fuzziness of behaviors of residential users, limitation of long-term portrayal and the like, a demand response adaptation user optimization mechanism considering double time scales is constructed; firstly, dividing power utilization modes by adopting a spectral clustering algorithm, and evaluating and sequencing long-term comprehensive availability; secondly, establishing short-term availability evaluation indexes by using the curve mean value and the time interval distribution condition of load concentration in the historical period, and arranging the short-term availability evaluation indexes in a descending order; finally, weighting the long-term and short-term evaluation results, and determining a weight ratio by quantifying the relative reliability of the usability ranking of the two;
s3, for price type preferred users, adopting a fuzzy clustering model to reconstruct the electricity price time period; firstly, considering a consumer psychological structure response willingness function, and representing an incidence relation with electricity price change; secondly, the nonlinear transfer rate characteristic in the actual response process is clarified, and a Logistic function is adopted to describe the price type fuzzy response willingness of the preferred user; finally, a Pairs Sum clustering model is used for depicting a peak-valley level time period, so that an actual load curve is quantized;
s4, integrating the identification modes of price type and excitation type differential demand response schemes, and establishing a two-time-period source load interaction robust decision framework in the day-ahead and day; in the day-ahead stage, wind and light prediction output is considered, the lowest decision cost is taken as a target, and a prediction decision scheme taking 1h as a time scale is obtained through optimization by combining a price type optimized user fuzzy load transfer rate; constructing a daily regulation and control model considering wind-light intermittency and excitation adaptation requirements to respond to users by taking the lowest regulation and control cost as a target in the daily stage; and finally, performing interactive iterative solution on the model by adopting second-order model relaxation, a linear dual algorithm and a column constraint theory, and comprehensively integrating the economy and the robustness of the scheduling result on the premise of meeting the wind and light total absorption.
The step S1 includes:
s11: determining resident user daily load mode event probability characteristic image
Digital feature images of diversified power consumption behaviors of the residential users are realized by extracting the common daily load pattern of the residential users and counting the occurrence probability of each daily pattern; if the extracted daily load patterns are divided into L types, the occurrence probability and the event probability characteristic of the user u daily load pattern L in one year are plotted like gamma u Comprises the following steps:
γ u ＝[γ u,1 ,γ u,2 ,…,γ u,L ] (2)
wherein, γ u,l The occurrence probability of the u-day load mode l for the user; n is a radical of l The occurrence frequency of the daily load pattern l;
s12: determining a resident user daily load pattern reference capacity
Step a 1: assuming the user historical load data prediction error sample data as
Calculating the distance between different probability distributions based on the Wasserstein algorithm:
wherein, W (P) 1 ,P 2 ) Is two probability distributions P 1 And P 2 The Wasserstein distance between; i | · | | is a norm; xi 1 And xi 2 Obey to probability distribution P 1 And P 2 ；Π(d(ξ 1 ),d(ξ 2 ) Is edge distribution of P 1 And P 2 A joint distribution probability of (a);
step a 2: constructed according to Wasserstein distance to
As the center, ε is the fuzzy uncertainty set of radii Ω:
wherein M (xi) is a xi support
All probability distributions of (a); β is the confidence level;
is the sample average;
step a 3: carrying out standardization processing on the sample data set, and constructing a data driving support set xi;
wherein, the first and the second end of the pipe are connected with each other,
is a sample covariance matrix;
is that
The s-th element of (1); l is
The boundary of (2);
step a 4: constructing a data driving support set b:
wherein, b max Is the upper boundary; p is a radical of std 、
Is composed of
True and empirical distribution of; phi std Is composed of
A fuzzy set of (1); phi represents a higher confidence level;
step a 5: finding based on interval boundaries
Obtaining the daily load mode reference capacity P u,l ：
S13: constructing a demand response suitability sketch
From the results of S11-S12, an integrated benchmark demand response availability β describing the user u load pattern l is described u,l And classifying the user groups by combining a spectral clustering algorithm, and defining the sum of the demand response availability of each mode of the user u as the demand response comprehensive availability Ψ u And further constructing a user-incentive usability portrait:
β u,l ＝γ u,l ·P u,l (12)
the step S2 includes:
s21: responding the availability F according to the comprehensive demand of the residential users u And arranging all users according to a descending order to obtain a long-term availability ranking set of each resident user:
E(U)＝{e u ＝ordering(Ψ u )|u∈U} (14)
wherein e is u Ranking the results for user u's long term availability with a value of Ψ u Arranging the names in descending order;
s22: calculating short-term demand response availability evaluation indexes of each residential user based on the hypothetical load prediction result and the time interval distribution of the response signal on the day
Wherein, J u,d,τ 、S v Peak load and demand response event occurrence status for user u; epsilon is a value scoring function for demand response energy; omicron is a scale factor; eta is a translation factor and controls the distribution position of the sensitivity unsaturated zone;
then, the short-term demand response availability evaluation indexes are sorted in descending order, and the formula is as follows:
S(U)＝{s u ＝ordering(Ψ u )|u∈U} (17)
wherein s is u Ordering the results for the short term availability of user u with a value equal to Ψ u Arranging the names in descending order;
s23: by counting the difference of the similar user clusters in the aspect of short-term and long-term demand response availability sequencing, the relative reliability of the user clusters and the short-term and long-term demand response availability sequencing is quantified, a reasonable weight coefficient theta is formulated to construct an excitation type potential user preferred index, and the formula is as follows:
the step S3 includes:
s31: according to the consumer psychology definition load transfer rate lambda as the ratio of price type preferred load transfer amount to electricity price difference value delta p, the traditional linear load transfer rate function formula is as follows:
wherein l ab Is a dead zone threshold; h is ab Is a saturation region threshold; lambda [ alpha ] max The maximum load transfer rate;
s32: adopting a Logistic function to construct a load fuzzy response willingness nonlinear model; lambda [ alpha ] pv Peak-to-valley load transfer rate; m is optimistic membership; delta P pv Peak to valley electrovalence difference; the fuzzy response equation is as follows:
in the dead zone part, the user has poor enthusiasm, and the optimistic and pessimistic curve average value can be adopted to represent the response willingness; in the saturation region, because the optimistic curve and the pessimistic curve are superposed, fitting the maximum load transfer rate representation; in the 'response area', the user response changes along with the electricity price difference and tends to an optimistic curve along with the increase of the electricity price difference, and a partial semi-trapezoidal membership function is adopted for calculation:
wherein a, c and mu are constants;
fitting values for the fuzzy load transfer rate;
and
respectively optimistic load transfer rate and pessimistic load transfer rate; the fuzzy load transfer rate of peak to flat and flat to valley can be obtained by the same method
And with
S33: a fuzzy model of load transfer rate is synthesized, a Pairs Sum clustering model is used for depicting a peak-valley flat time period under an uncertain condition, and an actual load curve passing through the peak-valley time period is further quantized; the time interval division basic flow is as follows:
step b 1: setting the total time interval as T, and setting i and j as the starting time and the ending time of a certain time interval respectively; continuity issues are considered in the divided period:
(1) if the starting time i is less than the ending time j, the time k (i is more than k and less than j) belongs to the same time interval;
(2) if the starting time i is greater than the ending time j, the time interval ranges from i to k being less than or equal to T and from 1 to k being less than or equal to j;
step b 2: normalizing the distance between the objects i and j by using Euclidean distance:
step b 3: clustering the time interval to be divided into K typical time intervals by adopting a Pairs Sum model, wherein the target function is as follows:
finally, solving by adopting a branch-and-bound method; by setting a parameter K and combining a fuzzy load transfer rate function, solving a time interval division scheme under the difference condition;
step b 4: setting the original load of a price type user i as L i (t) accounting for the load after the fuzzy load transfer rate response during the peak-to-valley period established in step b3
Comprises the following steps:
in the formula: t is p 、T f 、T v The time period sets of peak, flat and valley;
response preload averages.
The step S4 includes:
s41: the method comprises the following steps of (1) constructing a day-ahead-day double-layer flexible robust optimization decision-making basic model:
in the formula: x is a day-ahead decision variable; y is an intra-day regulation variable; u is a random parameter; the rest are constant matrixes;
s42: based on wind-solar output and load predicted values, a day-ahead power utilization optimization model considering network security constraints and price type adaptive user load transfer rate is constructed by taking the day-ahead scheduling cost as a target, and the formula is as follows:
wherein, Δ t is a time step; n is a radical of hydrogen MT Number of gas turbine units; n is a radical of PDR Responding to the load quantity for participating in the price type demand; a is MT And b MT Is the cost factor of the gas turbine unit; p MT,j (t) is the output electric power of gas turbine j during the time t of day; sigma (t) is the peak-to-valley time-of-use electricity price;
purchasing the electricity selling unit price for the upper-level power grid at the time t before the day;
and
purchasing electric power for the upper-level power grid in the day ahead;
s43: under the actual output of the scheduling solar wind and light, a solar power utilization regulation and control model is established, and the formula is as follows:
wherein the content of the first and second substances,
for gas turbine j to be adjusted up and downControlling unit price;
regulating and controlling power for the gas turbine j up and down; lambda Wind,j 、λ Solar,j Punishing cost unit price for wind and light;
injecting power into the wind and light generating set for a period t;
and
purchasing electricity selling unit prices for gateways in the period of t time within a day;
and
purchasing power for sale at a gateway at a time t; n is a radical of IDR Indicating a transferable load number of incentive type preferred users;
compensating the price coefficient for a unit;
represents the t period transfer power;
s44: combining S41-S43, the solving steps for constructing the double-layer interactive robust decision model are as follows:
step c 1: based on an adjustable robust optimization method, the random intermittent characterization of wind and light output by using a box type set is as follows:
considering that the boundary is difficult to obtain in the continuous regulation and control time period, introducing a preassigned gamma to restrain the wind and light force:
step c 2: decoupling an original model into a main sub-problem by adopting a column constraint algorithm, and processing the sub-problem based on a linear dual theory and a large M normal linearity, wherein the main sub-problem is finally expressed by a formula as follows:
wherein alpha, beta and gamma are dual variables, and xi is an auxiliary variable; u. of up 、u down Is the wind-light output set limit; xi + 、ξ - Positive and negative values representing xi;
is 0-1 auxiliary variable; when in use
The number of the carbon atoms is 1,
when the wind-solar output is 0, the wind-solar output is taken to the upper bound, xi i Is positive, and conversely takes a lower bound, ξ i If the two are negative, the result is a predicted value if the two are 0;
step c 3: aiming at the main and sub problem iterative solution, firstly, a main problem is solved by assuming a certain wind-solar output scene as an initial scene; solving the subproblems to obtain the corresponding worst output scene, and adding new constraints; the main problem is solved again after a new severe scene is obtained, and iteration is carried out until convergence is reached; with the iteration, the renewable energy output scene representing the worst situation is iterated continuously, and the result of the main problem is the robust scheme optimized by the current power utilization strategy.
The invention has the beneficial effects that: the incentive type adaptive image method based on the user electricity consumption behavior analysis result effectively analyzes the association and the group commonality characteristics between the user energy use habits and the energy use requirements, and is beneficial to accurately identifying the high-potential target users; the fuzzy set of historical load data distribution is constructed based on data driving, so that the reference capacity of a user in a daily load mode can be effectively reflected, and accurate portrait of the excitation type demand response adaptability is realized; compared with the optimization method under a single time scale, the excitation type potential user optimization mechanism under the double time scales can effectively solve the problems of large daily electric behavior fluctuation, limitation of a long-term scale portrait method in processing small-probability events and the like, the relative reliability of usability sequencing can be effectively quantified by counting the usability difference of a user cluster in a class under the double time scales, and the optimization decision precision of demand response potential users is improved; compared with the existing time interval clustering division algorithm, the fuzzy clustering model reconstruction time-of-use electricity price time interval can well solve the continuity problem of the scheduling time interval, can be applied to different random scenes, fully exerts the capacity of adjusting price load curves by the time-of-use electricity price, improves the source charge matching level and further improves the response willingness of a user; compared with a result-driven optimization algorithm, the proposed day-ahead-day two-period source-load interactive robust decision method improves the influence of wind-light output random intermittency on a decision scheme by a budget robustness uncertain set extraction technology, integrates the economy and the robustness of a scheduling result, and improves the wind-light absorption level as much as possible.
Drawings
Fig. 1 is an overall method roadmap of the present invention.
FIG. 2 is a diagram of the fuzzy nonlinear load transfer rate of the present invention.
FIG. 3 is a schematic diagram of a two-stage source-load interaction framework in a day-ahead manner.
FIG. 4 is a schematic diagram of a two-stage source-load interaction robust solving process according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention comprises the steps of: s1, aiming at the diversified trend of the power consumption behaviors of the user, constructing a characteristic analysis model of the daily load patterns of the user by extracting the common patterns of the user and analyzing the occurrence probability of the user on different daily load patterns, and representing the diversified power consumption behaviors; constructing a user data fuzzy set based on Wasserstein distance to accurately reflect user reference capacity; formulating an image based on occurrence probability characteristics and reference capacity of various modes of a user;
s2, aiming at the problems of fuzziness of behaviors of residential users, limitation of long-term portrayal and the like, a demand response adaptation user optimization mechanism considering double time scales is constructed; firstly, dividing the power utilization mode by adopting a spectral clustering algorithm, and evaluating and sequencing long-term comprehensive availability; secondly, establishing short-term availability evaluation indexes by using the curve mean value and the time interval distribution condition of load concentration in the historical period, and arranging the short-term availability evaluation indexes in a descending order; finally, weighting the long-term and short-term evaluation results, and determining a weight ratio by quantifying the relative reliability of the usability ranking of the two;
s3, for price type preferred users, adopting a fuzzy clustering model to reconstruct the electricity price time period; firstly, considering a consumer psychological structure response willingness function, and representing an incidence relation with electricity price change; secondly, the nonlinear transfer rate characteristic in the actual response process is clarified, and a Logistic function is adopted to describe the price type fuzzy response willingness of the preferred user; finally, a Pairs Sum clustering model is used for depicting a peak-valley level time period, so that an actual load curve is quantized;
s4, integrating the identification modes of price type and excitation type differential demand response schemes, and establishing a two-time-period source load interaction robust decision framework in the day-ahead and day; in the day-ahead stage, wind and light prediction output is considered, the lowest decision cost is taken as a target, and a prediction decision scheme taking 1h as a time scale is obtained through optimization by combining a price type optimized user fuzzy load transfer rate; in the day stage, a day regulation and control model which takes the wind-light intermittency and the excitation adaptation requirement as the response user is constructed by taking the lowest regulation and control cost as the target; and finally, performing interactive iterative solution on the model by adopting second-order model relaxation, a linear dual algorithm and a column constraint theory, and comprehensively integrating the economy and the robustness of the scheduling result on the premise of meeting the wind-solar total absorption as much as possible.
The step S1 includes:
s11: determining resident user daily load mode event probability characteristic image
Digital characteristic images of diversified power consumption behaviors of residential users are realized by extracting the common daily load patterns of the residential users and counting the occurrence probability of each daily pattern; if the extracted daily load patterns are divided into L types, the occurrence probability and the event probability characteristic of the user u daily load pattern L in one year are plotted like gamma u Comprises the following steps:
γ u ＝[γ u,1 ,γ u,2 ,…,γ u,L ] (2)
wherein, gamma is u,l The occurrence probability of the u-day load mode l for the user; n is a radical of l The occurrence frequency of the daily load pattern l;
s12: determining a resident user daily load pattern reference capacity
Step a 1: assuming the user historical load data prediction error sample data as
Calculating the distance between different probability distributions based on the Wasserstein algorithm:
wherein, W (P) 1 ,P 2 ) Is two probability distributions P 1 And P 2 The Wasserstein distance between; i | · | | is a norm; xi 1 And xi 2 Obey to probability distribution P 1 And P 2 ；Π(d(ξ 1 ),d(ξ 2 ) Is edge distribution of P 1 And P 2 A joint distribution probability of (a);
step a 2: constructed according to Wasserstein distance to
As the center, ε is the fuzzy uncertainty set of radii Ω:
wherein M (xi) is a xi support
All probability distributions of (a); β is the confidence level;
is the sample average;
step a 3: carrying out standardization processing on the sample data set, and constructing a data driving support set xi;
wherein the content of the first and second substances,
a sample covariance matrix is obtained;
is that
The s-th element of (1); l is
The boundary of (2);
step a 4: constructing a data driving support set b:
wherein, b max Is the upper boundary; p is a radical of std 、
Is composed of
True and empirical distribution of; phi std Is composed of
A fuzzy set of (1); phi represents a higher confidence level;
step a 5: finding based on interval boundaries
Obtaining the daily load mode reference capacity P u,l ：
S13: constructing a demand response suitability sketch
From the results of S11-S12, an integrated benchmark demand response availability β describing the user u load pattern l is described u,l And classifying the user groups by combining a spectral clustering algorithm, and defining the sum of the demand response availability of each mode of the user u as the comprehensive availability of the demand responseΨ u And further constructing a user-incentive usability portrait:
β u,l ＝γ u,l ·P u,l (12)
the step S2 includes:
s21: responding the availability F according to the comprehensive demand of the residential users u And arranging all users according to a descending order to obtain a long-term availability ranking set of each resident user:
E(U)＝{e u ＝ordering(Ψ u )|u∈U} (14)
wherein e is u Ranking the results for user u's long term availability with a value of Ψ u Rank in descending order;
s22: calculating short-term demand response availability evaluation indexes of each residential user based on the hypothetical load prediction result and the time interval distribution of the response signal on the day
Wherein, J u,d,τ 、S v Peak load and demand response event occurrence status for user u; epsilon is a value scoring function for demand response energy; omicron is a scale factor; eta is a translation factor and controls the distribution position of the sensitivity unsaturated zone;
then, the short-term demand response availability evaluation indexes are sorted in descending order, and the formula is as follows:
S(U)＝{s u ＝ordering(Ψ u )|u∈U} (17)
wherein s is u Ordering the results for the short term availability of user u with a value equal to Ψ u Arranging the names in descending order;
s23: by counting the difference of the similar user clusters in the aspect of short-term and long-term demand response availability sequencing, the relative reliability of the user clusters and the short-term and long-term demand response availability sequencing is quantified, a reasonable weight coefficient theta is formulated to construct an excitation type potential user preferred index, and the formula is as follows:
the step S3 includes:
s31: according to the consumer psychology definition of the load transfer rate lambda as the ratio of the price type preferred load transfer amount to the electricity price difference value delta p, the traditional linear load transfer rate function formula is as follows:
wherein l ab Is a dead zone threshold; h is ab Is a saturation region threshold; lambda [ alpha ] max The maximum load transfer rate;
s32: adopting a Logistic function to construct a load fuzzy response willingness nonlinear model, as shown in FIG. 2; lambda [ alpha ] pv Peak-to-valley load transfer rate; m is optimistic membership; delta P pv Peak-to-valley current valence difference; the fuzzy response equation is as follows:
in the dead zone part, the user has poor enthusiasm, and the response will can be represented by an average value of optimistic and pessimistic curves; in the saturation region, because the optimistic curve and the pessimistic curve are superposed, fitting the maximum load transfer rate representation; in the 'response area', the user response changes along with the electricity price difference and tends to an optimistic curve along with the increase of the electricity price difference, and a partial semi-trapezoidal membership function is adopted for calculation:
wherein a, c and mu are constants;
fitting values for the fuzzy load transfer rate;
and
respectively optimistic load transfer rate and pessimistic load transfer rate; the fuzzy load transfer rate of peak to flat and flat to valley can be obtained by the same method
And with
S33: a fuzzy model of load transfer rate is synthesized, a Pairs Sum clustering model is used for depicting a peak-valley flat time period under an uncertain condition, and an actual load curve passing through the peak-valley time period is further quantized; the time interval division basic flow is as follows:
step b 1: setting the total time interval as T, and setting i and j as the starting time and the ending time of a certain time interval respectively; continuity issues need to be considered in dividing the period:
(1) if the starting time i is less than the ending time j, the time k (i is more than k and less than j) belongs to the same time interval;
(2) if the starting time i is greater than the ending time j, the time interval ranges from i to k being less than or equal to T and from 1 to k being less than or equal to j;
step b 2: normalizing the distance between the objects i and j by using Euclidean distance:
step b 3: clustering the time interval to be divided into K typical time intervals by adopting a Pairs Sum model, wherein the target function is as follows:
finally, solving by adopting a branch-and-bound method; by setting a parameter K and combining a fuzzy load transfer rate function, solving a time interval division scheme under the difference condition;
step b 4: setting the original load of a price type user i as L i (t) accounting for the load after the fuzzy load transfer rate response during the peak-to-valley period established in step b3
Comprises the following steps:
in the formula: t is p 、T f 、T v The time period sets of peak, flat and valley are obtained;
response preload averages.
The step S4 includes:
s41: considering intermittent uncertainty of output of renewable energy and nonlinear transfer rate of fuzzy load, a two-stage source load interaction framework in the day-ahead and day-in based on a robust scheduling strategy is constructed, as shown in fig. 3. Price type demand response is embedded in the day-ahead time period to guide the adaptive users to carry out peak clipping and valley filling, and the operating pressure of the power distribution network is reduced. The excited optimal user performs load translation according to contract requirements in the day period, and wind and light total absorption is realized as far as possible on the premise of meeting economy. And analyzing and making a safe and reliable source-load interaction plan by combining the day-ahead and day-in comprehensive operation cost, and quantitatively analyzing benefits brought by demand response. Therefore, the method for constructing the day-ahead-day double-layer flexible robust optimization decision-making basic model comprises the following steps:
in the formula: x is a day-ahead decision variable; y is an intra-day regulation variable; u is a random parameter; the rest are constant matrixes;
s42: based on wind-solar output and load predicted values, a day-ahead power utilization optimization model considering network security constraints and price type adaptive user load transfer rate is constructed by taking the day-ahead scheduling cost as a target, and the formula is as follows:
wherein, Δ t is a time step; n is a radical of MT Number of gas turbine units; n is a radical of PDR Responding to the load quantity for participating in the price type demand; a is MT And b MT Is the cost factor of the gas turbine unit; p MT,j (t) is the output electric power of gas turbine j during the time t of day; sigma (t) is the peak-to-valley time-of-use electricity price;
purchasing the electricity selling unit price for the upper-level power grid at the time t before the day;
and
purchasing electric power for the upper-level power grid in the day ahead;
(1) network security constraints
In the actual operation process, the load flow constraint of the whole network architecture is considered, the influence of reactive power is ignored, the voltage of the node is represented by a per unit value, and a sensitivity factor can be obtained, wherein the formula is as follows:
wherein the content of the first and second substances,
is U i The conjugate value of (a); y is a node admittance matrix; p is j Injected electrical power for node j;
(2) power balance constraint
(3) Gas turbine operating constraints
Wherein S is MT,j (t) gas turbine j operating condition for time t;
respectively representing j output limits of the gas turbine;
the power limit of the gas turbine j climbing up and down;
(4) electric energy storage restraint
Wherein, the first and the second end of the pipe are connected with each other,
a charge-discharge state at a time period t;
is the charging and discharging limit power;
capacity and limit;
the charging and discharging efficiency is improved;
(5) gateway interaction power constraints
Wherein the content of the first and second substances,
interaction and limit power for a period t;
is in an interactive state;
s43: under the actual output of the scheduling solar wind and light, a solar power utilization regulation and control model is established, and the formula is as follows:
wherein the content of the first and second substances,
punishing unit price for up-down regulation and control of the gas turbine j;
regulating and controlling power for the gas turbine j up and down; lambda [ alpha ] Wind,j 、λ Solar,j Punishing cost unit price for wind and light;
injecting power into the wind and light generating set for a period t;
and
purchasing electricity selling unit prices for gateways in the period of t time within a day;
and
purchasing power for selling electricity for the gate at the time t; n is a radical of IDR Indicating a transferable load number of incentive type preferred users;
compensating the price coefficient for a unit;
represents the t period transfer power;
(1) network security constraints
The network security constraint is the same as that of the previous stage;
(2) intra-day power balance constraint
In the formula:
purchasing power for sale within a day of time t;
(3) renewable energy unit output constraint
(4) Daily regulation and control constraint of controllable unit
Wherein the content of the first and second substances,
the up-down regulation state of the gas turbine j at the time t;
the upper and lower regulation limits of the gas turbine j at the time t;
(5) intra-day stage gateway interaction power constraint
Wherein the content of the first and second substances,
purchasing power selling states in the regulation and control stage at the time t;
(6) incentive type demand response constraints
Wherein the content of the first and second substances,
the load transferring in/out and the limit of the excitation type preferred user j in the time period t;
a t-period response state;
s44: combining S41-S43, the solving steps for constructing the double-layer interactive robust decision model are as follows:
step c 1: based on an adjustable robust optimization method, the random intermittent characterization of wind and light output by using a box type set is as follows:
considering that the boundary is difficult to obtain in the continuous regulation and control time period, introducing a preassigned gamma to restrain the wind and light force:
step c 2: decoupling an original model into a main sub-problem by adopting a column constraint algorithm, and processing the sub-problem based on a linear dual theory and a large M normal linearity, wherein the main sub-problem is finally expressed by a formula as follows:
wherein alpha, beta and gamma are dual variables, and xi is an auxiliary variable; u. of up 、u down Is the wind-light output set limit; xi + 、ξ - Positive and negative values representing xi;
is 0-1 auxiliary variable; when in use
The number of the carbon atoms is 1,
when the wind-light output is 0, the wind-light output reaches the upper bound xi i Is positive, and conversely takes a lower bound, ξ i If the two are negative, the result is a predicted value if the two are 0;
step c 3: aiming at the main and sub problem iterative solution, firstly, a main problem is solved by assuming a certain wind-solar output scene as an initial scene; solving the subproblems to obtain the corresponding worst output scene, and adding new constraints; the main problem is solved again after a new severe scene is obtained, and iteration is carried out until convergence is reached; with the iteration, the renewable energy output scene representing the worst situation is continuously iterated, the result of the main problem is the robust scheme optimized by the current power utilization strategy, and the specific solving steps are shown in fig. 4.
The incentive type adaptive image method based on the user electricity consumption behavior analysis result effectively analyzes the association and the group commonality characteristics between the user energy use habits and the energy use requirements, and is favorable for accurately identifying high-potential target users; the fuzzy set of historical load data distribution is constructed based on data driving, so that the reference capacity of a user in a daily load mode can be effectively reflected, and accurate portrait of the excitation type demand response adaptability is realized; compared with an optimization method under a single time scale, the excitation type potential user optimization mechanism under the double time scales can effectively solve the problems of large daily electric behavior volatility, limitation of a long-term scale portrait method in processing small-probability events and the like, the relative reliability of usability sequencing can be effectively quantized by counting the usability difference of user clusters in the class under the double time scales, and the optimization decision precision of demand response potential users is improved; compared with the existing time interval clustering division algorithm, the fuzzy clustering model reconstruction time-of-use electricity price time interval can well solve the continuity problem of the scheduling time interval, can be applied to different random scenes, fully exerts the capacity of adjusting price load curves by the time-of-use electricity price, improves the source charge matching level and further improves the response willingness of a user; compared with a result-driven optimization algorithm, the proposed day-ahead-day two-period source-load interactive robust decision method improves the influence of wind-light output random intermittency on a decision scheme by a budget robustness uncertain set extraction technology, integrates the economy and the robustness of a scheduling result, and improves the wind-light absorption level as much as possible.
Claims (5)
1. A residential customer electricity optimization strategy taking into account a differentiated demand response scheme, comprising the steps of:
s1, aiming at the diversified trend of the power consumption behaviors of the user, constructing a characteristic analysis model of the daily load patterns of the user by extracting the common patterns of the user and analyzing the occurrence probability of the user on different daily load patterns, and representing the diversified power consumption behaviors; constructing a user data fuzzy set based on Wasserstein distance to accurately reflect user reference capacity; formulating an image based on occurrence probability characteristics and reference capacity of various modes of a user;
s2, aiming at the problems of fuzziness of behaviors of residential users, limitation of long-term portrayal and the like, a demand response adaptation user optimization mechanism considering double time scales is constructed; firstly, dividing power utilization modes by adopting a spectral clustering algorithm, and evaluating and sequencing long-term comprehensive availability; secondly, establishing short-term availability evaluation indexes by using the curve mean value and the time interval distribution condition of load concentration in the historical period, and arranging the short-term availability evaluation indexes in a descending order; finally, weighting the long-term and short-term evaluation results, and determining a weight ratio by quantifying the relative reliability of the usability ranking of the two;
s3, for price type preferred users, adopting a fuzzy clustering model to reconstruct the electricity price time period; firstly, considering a consumer psychological structure response willingness function, and representing an incidence relation with electricity price change; secondly, the nonlinear transfer rate characteristic in the actual response process is clarified, and a Logistic function is adopted to describe the price type fuzzy response willingness of the preferred user; finally, a Pairs Sum clustering model is used for depicting a peak-valley level time period, so that an actual load curve is quantized;
s4, integrating the identification modes of price type and excitation type differential demand response schemes, and establishing a two-time-period source load interaction robust decision framework in the day-ahead and day; in the day-ahead stage, wind and light prediction output is considered, the lowest decision cost is taken as a target, and a prediction decision scheme taking 1h as a time scale is obtained through optimization by combining a price type optimized user fuzzy load transfer rate; in the day stage, a day regulation and control model which takes the wind-light intermittency and the excitation adaptation requirement as the response user is constructed by taking the lowest regulation and control cost as the target; and finally, performing interactive iterative solution on the model by adopting second-order model relaxation, a linear dual algorithm and a column constraint theory, and comprehensively integrating the economy and the robustness of the scheduling result on the premise of meeting the wind and light total absorption.
2. The electricity optimization strategy for residential users considering differential demand response schemes as claimed in claim 1, wherein said step S1 comprises:
s11: determining resident user daily load mode event probability characteristic image
Digital characteristic images of diversified power consumption behaviors of residential users are realized by extracting the common daily load patterns of the residential users and counting the occurrence probability of each daily pattern; if the extracted daily load patterns are divided into L types, the occurrence probability and the event probability characteristic of the user u daily load pattern L in one year are plotted like gamma u Comprises the following steps:
γ u ＝[γ u,1 ,γ u,2 ,…,γ u,L ] (2)
wherein, γ u,l The occurrence probability of the u-day load mode l for the user; n is a radical of l The occurrence frequency of the daily load pattern l;
s12: determining resident user daily load pattern reference capacity
Step a 1: assuming the user historical load data prediction error sample data as
Calculating the distance between different probability distributions based on the Wasserstein algorithm:
wherein，W(P 1 ,P 2 ) Is two probability distributions P 1 And P 2 The Wasserstein distance between; i | · | | is a norm; xi 1 And xi 2 Obey to probability distribution P 1 And P 2 ；Π(d(ξ 1 ),d(ξ 2 ) Is edge distribution of P 1 And P 2 A joint distribution probability of (a);
step a 2: constructed according to Wasserstein distance to
As a center, ε is the fuzzy uncertainty set of radii Ω:
wherein M (xi) is a xi support
All probability distributions of (a); β is the confidence level;
is the sample average;
step a 3: carrying out standardization processing on the sample data set, and constructing a data driving support set xi;
wherein, the first and the second end of the pipe are connected with each other,
is a sample covariance matrix;
is that
The s-th element of (1); l is
The boundary of (2);
step a 4: constructing a data driving support set b:
wherein, b max Is the upper boundary; p is a radical of std 、
Is composed of
True and empirical distribution of; phi std Is composed of
A fuzzy set of (1); phi represents a higher confidence level;
step a 5: finding based on interval boundaries
Obtaining the daily load mode reference capacity P u,l ：
S13: constructing a demand response suitability sketch
From the results of S11-S12, an integrated benchmark demand response availability β describing the user u load pattern l is described u,l And classifying the user groups by combining a spectral clustering algorithm, and defining the sum of the demand response availability of each mode of the user u as the demand response comprehensive availability Ψ u And further constructing a user-incentive usability portrait:
β u,l ＝γ u,l ·P u,l (12)
3. the electricity optimization strategy for residential users considering differential demand response schemes as claimed in claim 1, wherein said step S2 comprises:
s21: responding the availability F according to the comprehensive demand of the residential users u And arranging all users according to a descending order to obtain a long-term availability ranking set of each resident user:
E(U)＝{e u ＝ordering(Ψ u )|u∈U} (14)
wherein e is u Is the long term availability ranking result of user u with a value of Ψ u Arranging the names in descending order;
s22: calculating short-term demand response availability evaluation indexes of each residential user based on the hypothetical load prediction result and the time interval distribution of the response signal on the day
Wherein, J u,d,τ 、S v Peak load and demand response event occurrence status for user u; epsilon is a value scoring function for demand response energy; omicron is a scale factor; eta is a translation factor and controls the distribution position of the sensitivity unsaturated zone;
then, the short-term demand response availability evaluation indexes are sorted in descending order, and the formula is as follows:
S(U)＝{s u ＝ordering(Ψ u )|u∈U} (17)
wherein s is u Ordering the results for the short term availability of user u with a value equal to Ψ u Arranging the names in descending order;
s23: by counting the difference of similar user clusters in the aspect of short-term and long-term demand response availability sequencing, the relative reliability of the user clusters and the short-term and long-term demand response availability sequencing is quantified, a reasonable weight coefficient theta is formulated to construct an excitation type potential user preferred index, and the formula is as follows:
4. the electricity optimization strategy for residential users considering differential demand response schemes as claimed in claim 1, wherein said step S3 comprises:
s31: according to the consumer psychology definition load transfer rate lambda as the ratio of price type preferred load transfer amount to electricity price difference value delta p, the traditional linear load transfer rate function formula is as follows:
wherein l ab Is a dead zone threshold; h is ab Is a saturation region threshold; lambda [ alpha ] max The maximum load transfer rate;
s32: adopting a Logistic function to construct a load fuzzy response willingness nonlinear model; lambda pv Peak-to-valley load transfer rate; m is optimistic membership; delta P pv Peak-to-valley current valence difference; the fuzzy response equation is as follows:
in the dead zone part, the user has poor enthusiasm, and the optimistic and pessimistic curve average value can be adopted to represent the response willingness; in the saturation region, because the optimistic curve and the pessimistic curve are superposed, fitting the maximum load transfer rate representation; in the 'response area', the user response changes along with the electricity price difference and tends to an optimistic curve along with the increase of the electricity price difference, and a partial semi-trapezoidal membership function is adopted for calculation:
wherein a, c and mu are constants;
fitting values for the fuzzy load transfer rate;
and
respectively optimistic load transfer rate and pessimistic load transfer rate; the fuzzy load transfer rate of peak to flat and flat to valley can be obtained by the same method
And with
S33: a fuzzy model of load transfer rate is synthesized, a Pairs Sum clustering model is used for depicting a peak-valley flat time period under an uncertain condition, and an actual load curve passing through the peak-valley time period is quantized; the time interval division basic flow is as follows:
step b 1: setting the total time interval as T, and setting i and j as the starting time and the ending time of a certain time interval respectively; continuity issues are considered in the divided period:
(1) if the starting time i is less than the ending time j, the time k (i is more than k and less than j) belongs to the same time interval;
(2) if the starting time i is greater than the ending time j, the time interval ranges from i to k being less than or equal to T and from 1 to k being less than or equal to j;
step b 2: normalizing the distance between the objects i and j by using Euclidean distance:
step b 3: clustering the time interval to be divided into K typical time intervals by adopting a Pairs Sum model, wherein the target function is as follows:
finally, solving by adopting a branch-and-bound method; by setting a parameter K and combining a fuzzy load transfer rate function, solving a time interval division scheme under the difference condition;
step b 4: setting the original load of a price type user i as L i (t) accounting for the load after the fuzzy load transfer rate response during the peak-to-valley period established in step b3
Comprises the following steps:
in the formula: t is p 、T f 、T v The time period sets of peak, flat and valley;
response preload averages.
5. The electricity optimization strategy for residential users considering differential demand response schemes as claimed in claim 1, wherein said step S4 comprises:
s41: the method comprises the following steps of (1) constructing a day-ahead-day double-layer flexible robust optimization decision-making basic model:
in the formula: x is a day-ahead decision variable; y is an intra-day regulation variable; u is a random parameter; the rest are constant matrixes;
s42: based on wind-solar output and load predicted values, a day-ahead power utilization optimization model considering network security constraints and price type adaptive user load transfer rate is constructed by taking the day-ahead scheduling cost as a target, and the formula is as follows:
wherein, Δ t is a time step; n is a radical of MT Number of gas turbine units; n is a radical of PDR Responding to the load quantity for participating in the price type demand; a is MT And b MT Is the cost factor of the gas turbine unit; p MT,j (t) is the output electric power of gas turbine j during the time t of day; sigma (t) is the peak-to-valley time-of-use electricity price;
purchasing the electricity selling unit price for the upper-level power grid at the time t before the day;
and
purchasing electric power for the upper-level power grid in the day ahead;
s43: under the actual output of the scheduling solar wind and light, a solar power utilization regulation and control model is established, and the formula is as follows:
wherein the content of the first and second substances,
punishing unit price for up-down regulation and control of the gas turbine j;
regulating and controlling power for the gas turbine j up and down; lambda [ alpha ] Wind,j 、λ Solar,j Punishing cost unit price for wind and light;
injecting power into the wind and light generating set for a period t;
and
purchasing electricity selling unit prices for gateways in the period of t time within a day;
and
purchasing power for sale at a gateway at a time t; n is a radical of IDR Indicating a transferable load number of incentive type preferred users;
compensating the price coefficient for a unit;
represents the t period transfer power;
s44: combining S41-S43, the solving steps for constructing the double-layer interactive robust decision model are as follows:
step c 1: based on an adjustable robust optimization method, the random intermittent characterization of wind and light output by using a box type set is as follows:
considering that the boundary is difficult to obtain in the continuous regulation and control time period, introducing a preassigned gamma to restrain the wind and light force:
step c 2: decoupling an original model into a main sub-problem by adopting a column constraint algorithm, and processing the sub-problem based on a linear dual theory and a large M normal linearity, wherein the main sub-problem is finally expressed by a formula as follows:
wherein, alpha, beta and gamma are dual variables, and xi is an auxiliary variable; u. of up 、u down Is the wind-light output set limit; xi + 、ξ - Positive and negative values representing xi;
is 0-1 auxiliary variable; when in use
The number of the carbon atoms is 1,
when the wind-solar output is 0, the wind-solar output is taken to the upper bound, xi i Is positive, and conversely takes a lower bound, ξ i If the two are negative, the result is a predicted value if the two are 0;
step c 3: aiming at the main and sub-problems, iterative solution is carried out, and firstly, a main problem is solved by assuming a certain wind-solar output scene as an initial scene; solving the subproblems to obtain the corresponding worst output scene, and adding new constraints; the main problem is solved again after a new severe scene is obtained, and iteration is carried out until convergence is reached; with the iteration, the renewable energy output scene representing the worst situation is continuously iterated, and the result of the main problem is the robust scheme optimized by the current power utilization strategy.
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