CN107748944B - User side demand response method under power selling side discharge environment - Google Patents
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Abstract
The invention discloses a user side demand response method under an electricity selling side discharge environment. The main basis for formulating demand response strategies for industrial and commercial users and other users is the daily load curve of the electricity customers, and the daily load curve of the users is not decomposed, so that the demand influence strategies formulated according to the macroscopic daily load curve of the users are caused, and the actual effect is not ideal in the implementation process. According to the method, a large number of users are divided through a clustering algorithm, and a demand response strategy is formulated for the same class of users; when the demand response strategy is formulated, the daily load curve of the user is regarded as being composed of a basic load, a transferable load, a reducible load and a storable load, and then the demand response strategy is formulated respectively for each component. The invention greatly reduces the working intensity of the electricity selling company or the power grid enterprise, and improves the working efficiency and the working accuracy; the demand response strategy formulated by the invention is more accurate and efficient.
Description
Technical Field
The invention relates to an electric power system, in particular to a user side demand response method under an electricity selling side discharge environment.
Background
The role of consumers of electricity was valued since the 70 s of the twentieth century, when the oil crisis first of all realized by economists that, from a social point of view, managing the demand of electricity is a more rational measure than increasing the supply of electricity if the economic losses or inconveniences of saving electricity are less than the cost of producing such electricity. In the beginning of the 80 s to the 90 s of the twentieth century, power Demand Side Management (DSM) is gradually popularized in major industrial countries, and by taking various effective measures such as laws, economy, technology, Management and guidance and taking power companies as main bodies, power users are guided to optimize power utilization modes and improve terminal power utilization efficiency, so that resource allocation is optimized and the environment is improved and protected.
In the early 90 s of the last century, market reformation of the power industry began worldwide, and the market mechanism gradually became the core content of the operation mode of the power system. The reformation of the problems in the initial power market, especially the power crisis in california in the year 2000, has prompted people to further consider the role of the power demand side and to form a consensus that: if the necessary mechanism is not designed for the demand side at the beginning of the electric power market construction, the cost is much higher when the problem is solved. Therefore, the introduction of Demand Response (Demand Response) in power market competition, i.e. guiding users to participate in the power market through price signals and economic incentive mechanisms to enhance the role of Demand side in market supply and Demand balance, becomes an important content of power market research and practice.
Some suggestions on further deepening the innovation of the power system (2015-9) (hereinafter referred to as "article 9"), "article 9": the deepening of the electric power system is an urgent task, and the energy safety and the economic and social development of China are all concerned. The 9 th article provides a new mission and new requirements for the advanced power system innovation. The key points and paths for deepening the power system innovation are defined as follows: on the basis of further perfecting the separation of government and enterprise, the separation of plant network and the separation of main and auxiliary, according to a system framework of managing the middle and releasing the two ends, sequentially releasing the electricity price of competitive links except for transmission and distribution, sequentially opening and distributing electricity selling business to social capital, and sequentially releasing electricity generating and using plans except for public welfare and regulation; the pushing transaction mechanism is relatively independent and operates normally; the regional power grid construction and the transmission and distribution system research suitable for the national conditions of China are further deepened; further strengthen the supervision, further strengthen electric power overall planning, further strengthen electric power safe high-efficient operation and reliable supply. The 9 th article also standardizes the operation mode of the power grid enterprise, and clearly indicates that the power grid enterprise does not use the price difference between the on-line price and the sale price as a main income source any more, and charges the net fee according to the approved transmission and distribution price, thereby ensuring the stable income source and income level of the power grid enterprise and standardizing the investment and asset management behaviors of the power grid enterprise.
Whether the power selling company or the power grid enterprise, the industrial and commercial users are mainly used as objects for implementing demand response. The enthusiasm of the electricity selling company or the power grid enterprise for implementing demand response to residential users is not high, mainly because:
(1) due to the fact that the number of the residential users is large, the power utilization load, the power utilization habits, the user experience levels and the like of different residential users are greatly different. A corresponding demand response strategy is respectively formulated for each residential user, and no power is provided for implementation by the electricity selling company or the power grid enterprise.
(2) The main basis for formulating demand response strategies for industrial and commercial users and other users is the daily load curve of the electricity customers, and the daily load curve of the users is not decomposed, so that the demand influence strategies formulated according to the macroscopic daily load curve of the users are caused, and the actual effect is not ideal in the implementation process.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art, and provide a user side demand response method in a power selling side open loop environment, which is a method for clustering users according to a plurality of dimensions such as power load, power utilization preference and the like of the users, a large number of users are clustered into several categories by using a clustering algorithm, and then an individual demand response strategy is formulated for each category of users; according to the basic strategy classification of user demand side response, the daily load curve is regarded as consisting of basic load, transferable load, reducible load and storable load, and the analysis of a large number of historical load curves under the same demand mode can realize the decomposition of the four types of load demand modes.
Therefore, the invention adopts the following technical scheme: a user side demand response method under a power selling side discharge environment is characterized in that a clustering algorithm is utilized, a plurality of dimensions including load curve characteristics, power utilization habits and user experience levels are selected, a large number of user power utilization load curves are clustered, users with similar power utilization load curve characteristics are clustered in the same class, and the user load curves in different classes are obviously different; dividing a large number of users through a clustering algorithm, and formulating a demand response strategy for the same class of users; when the demand response strategy is formulated, the daily load curve of the user is regarded as consisting of a basic load, a transferable load, a reducible load and a storable load, and then the demand response strategy is formulated respectively aiming at each component, so that the demand response strategy of the electricity consumer is more accurate and efficient.
Through a clustering algorithm, a large number of users are divided, and a demand response strategy is formulated for the same type of users, so that the working intensity of an electricity selling company or a power grid enterprise is greatly reduced, and the working efficiency and the working accuracy are improved.
Preferably, the clustering of the user electricity load curve is based on a K-MEANS algorithm, and includes dividing n data objects into K clusters, so that the sum of squares of data points in each cluster to the center of the cluster is the minimum, and the algorithm processing procedure is as follows:
inputting: a cluster number k, a data set containing n data objects; and (3) outputting: k clusters; the method comprises the following specific steps:
the first step is as follows: randomly selecting k objects from n data objects as initial clustering centers;
the second step is that: respectively calculating the distance from each data object to each cluster center, and distributing the data objects to the clusters with the closest distance;
the third step: after all the data objects are distributed, recalculating centers of the k clusters;
the fourth step: comparing with k clustering centers obtained by previous calculation, if the clustering centers change, turning to the second step, otherwise, turning to the fifth step;
the fifth step: and outputting a clustering result.
As a preferred aspect of the above technical solution, the demand response scheme in which the user responds to the dynamic electricity price by adjusting the electricity consumption behavior of the user includes the following three modes:
first, responding to electricity rates in the form of reducing electricity usage during peak electricity usage hours;
second, transferring the electricity usage during peak hours to off-peak hours;
third, electricity usage during peak hours is reduced, and electricity usage during peak hours is shifted to off-peak hours.
As a preferable mode of the above technical solution, based on the above three modes, the demand response scheme is formulated as follows:
the first type is: the demand characteristics of the electric equipment are only determined by the environmental state and the operation state of the current time period, the adjacent time periods have no relation, and the response of the electric equipment under the real-time electricity price is as follows:
s.t.p(t)＝d(t) 0≤d0(t)-d(t)≤ΔD(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is the real-time electricity price and comfort loss factor of the corresponding terminal, d0(t) is the original user power demand, and Δ d (t) is the maximum value of the power demand variation;
the second type: the demand characteristics of the electric equipment are independent of time, the electric equipment is turned on at any time in a day and then turned off after completing work for a certain period, and the optimal response behaviors of the electric equipment are as follows:
s.t.u(t+s)＝1ifu(t)＝1(s＝1,2,…,τ)，
wherein d (T) is the electric energy requirement, λ (T) and μ (T) are the real-time electricity price and the comfort loss factor of the corresponding terminal, U (T) is the switching control quantity of the third type of equipment, and U is the sum of the switching control quantity at T moments; τ represents the period length in h;
the third type: the demand characteristics of the electric equipment are determined not only by the environmental state and the operation state of the current time period, but also by the time coupling relation of the adjacent time periods, and the optimal response behaviors of the electric equipment are as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is the real-time electricity price and the comfort loss factor of the corresponding terminal, V (t) is the energy level when the electric equipment is used, and v (t) represents the part representing the energy coupling of the t time period and the t +1 time period;
is the upper limit of the energy level of the consumer; v is the lower energy level limit of the consumer.
Preferably, in the above-described technical means, the power consumption level of the electric equipment i is discretized according to the equipment parameters, and the discretized power consumption level is used as the state, and each power consumption level has the corresponding action;
assuming that the power consumption level of the electric equipment is just divided into H points, plus the shutdown state of the equipment, the operation state of the electric equipment is discretized into H +1 power consumption levels, and at time t, each power consumption level is taken as a state s (t) of the electric equipment, that is:
in the above formula, pi(t) represents the power utilization decision of the power utilization equipment i in the t period, and the unit is as follows: kW.h;Prepresents the lower limit of the electric power used by the user, and the unit is: kW; h representsH level in the divided H power utilization levels; Δ represents discretization with electrical level;
the state set s (t) { s) at time t0,s1,…,sH}，si(i-0, …, H) represents each state in the set of states;
when the state of the electric equipment is in s (t), the basis for the Agent to select the action to carry out the state transition is influenced by several factors: the current power price lambda level, the comfort requirement of the user and the influence of the electricity demand characteristics of the user; for the consumers of the second type, the energy level V of the device also needs to be taken into account.
As the optimization of the technical scheme, the decomposition of the four types of load demands is realized by analyzing a large number of historical load curves in the same demand mode;
1) base load
Cluster center vector p to classify k0 kThe curve is regarded as a trend curve of the user base load, and meanwhile, according to the user background situation, the response participation coefficient gamma of the user demand side is determined, so that the base load curve is as follows:
in the formula,
representing the original electricity demand of the kth class;
2) transferable load
The transferable load means that the requirements of the electric equipment are irrelevant at the same time, and the electric equipment is started at any time in one day and then is closed after finishing the work for a certain period of time according to a certain fixed mode;
the key point of the transferable load decomposition is the analysis and identification of load peaks and transfer quantity, and for the load peaks, the peak quantity and the peak center moment are determined according to the local maximum value of the basic load trend curve of the classification k
The range of transfer times for which the load can be transferred is then:
wherein,
and
respectively representing the peak transition boundary values derived from the user data;
the transferable load levels were:
in the formula (d)s(t) represents the total load demand function,
The required power consumption amount at the time of the lower limit transition,
Indicating the power demand at the time of transferring the upper limit;
3) can reduce the load
The load can be reduced as the variable part of the load after the basic load and the transferable load are removed, and the calculation formula is as follows:
in the formula,
respectively a reducible load, a total load demand function, a transferable load level curve and a basic load curve;
4) storable load
The storable load depends on whether the user has a corresponding storage device, the relevant parameters being considered as input quantities.
Preferably, the load curve classification and parameter setting are utilized to realize the online simulation of the responses of different user demand sides based on the actual power consumption data of the user;
the online simulation takes the state transition of different types of loads as a basic strategy and takes the randomly generated demand response intensity as an input parameter to realize the demand response strategy simulation of an online given user actual curve;
the three types of demand response strategies are implemented as follows:
transferable load
Wherein,
is a random variable;
the expression parameter is
Uniform distribution of (2);
can reduce the load
Wherein psiSIn order to be able to reduce the random variation of the load strength,
it is shown that the load can be reduced,
is shown in
ΨCAs a function of the parameter;
storable load
The response model of the storable load is as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
compared with the prior art, the invention has the following advantages: according to the invention, a large number of users are divided through a clustering algorithm, and a demand response strategy is formulated for the same type of users, so that the working intensity of an electricity selling company or a power grid enterprise is greatly reduced, the working efficiency is improved, and the working accuracy is improved. The demand response strategy is respectively formulated according to each component of the daily load curve of the user, so that the demand response strategy of the electricity consumer is more accurate and efficient.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Drawings
The invention is further described with reference to the drawings and the detailed description.
Interpretation of related terms
Demand side Response (DR), which is an abbreviation of power Demand Response, means that when the power wholesale market price increases or the system reliability is threatened, after a power consumer receives a direct compensation notification of an inductive reduction load or a power price increase signal sent by a power supplier, the power consumer changes an inherent conventional power mode, and the power consumer reduces or shifts the power consumption load for a certain period of time to respond to power supply, so that the power grid is stabilized, and the short-term behavior of power price increase is suppressed. It is one of the solutions for Demand Side Management (DSM).
Demand Side Management (DSM) refers to Management performed on the electricity consumer Side. The management is a method for guiding users to consume less power at peak and more power at valley by the state through policy measures, so that the power supply efficiency is improved and the power utilization mode is optimized. Therefore, the power consumption and the power demand can be reduced under the condition of completing the same power utilization function, so that the power shortage pressure is relieved, and the power supply cost and the power utilization cost are reduced. The power supply and the power utilization are economical, and the long-term purposes of saving energy and protecting the environment are achieved.
Releasing the electricity selling side:
the electricity selling side is opened, namely competition is introduced in an electricity selling link, a user is given a free option, and the method specifically comprises the following two aspects: the method comprises the following steps of firstly, releasing the free selection right of a user, and allowing the user to freely select an electricity selling company; and secondly, constructing a plurality of electricity selling main bodies, allowing all enterprises meeting the admission conditions to gradually engage in electricity selling business, and forming a plurality of electricity selling patterns.
K-Means clustering algorithm:
the K-means algorithm is a hard clustering algorithm, is a typical target function clustering method based on a prototype, takes a certain distance from a data point to the prototype as an optimized target function, and obtains an adjustment rule of iterative operation by using a function extremum solving method. The K-means algorithm takes Euclidean distance as similarity measure, and solves the optimal classification of a corresponding initial clustering center vector V, so that the evaluation index J is minimum. The algorithm uses a sum of squared errors criterion function as a clustering criterion function.
As shown in fig. 1, a user-side demand response method in an electricity-selling side open environment selects multiple dimensions including load curve characteristics, electricity usage habits and user experience levels, clusters a large number of user electricity load curves, and realizes that users with similar electricity load curve characteristics are clustered in the same class, so that the user load curves in different classes have obvious differences. Dividing a large number of users through a clustering algorithm, and formulating a demand response strategy for the same class of users; when the demand response strategy is formulated, the daily load curve of the user is regarded as being composed of a basic load, a transferable load, a reducible load and a storable load, and then the demand response strategy is formulated respectively for each component.
A large number of users are divided through a clustering algorithm, and a demand response strategy is formulated for the same class of users.
The clustering of the user electricity load curve is based on a K-MEANS algorithm, and comprises the steps of dividing n data objects into K clusters, so that the sum of squares from data points in each cluster to the center of the cluster is minimum, and the algorithm processing process is as follows:
inputting: a cluster number k, a data set containing n data objects; and (3) outputting: k clusters; the method comprises the following specific steps:
the first step is as follows: randomly selecting k objects from n data objects as initial clustering centers;
the second step is that: respectively calculating the distance from each data object to each cluster center, and distributing the data objects to the clusters with the closest distance;
the third step: after all the objects are distributed, recalculating centers of the k clusters;
the fourth step: comparing with k clustering centers obtained by previous calculation, if the clustering centers change, turning to the second step, otherwise, turning to the fifth step;
the fifth step: and outputting a clustering result.
The demand response scheme that the user responds to the dynamic electricity price by adjusting the own electricity consumption behavior comprises the following three modes:
first, responding to electricity rates in the form of reducing electricity usage during peak electricity usage hours;
second, transferring the electricity usage during peak hours to off-peak hours;
third, electricity usage during peak hours is reduced, and electricity usage during peak hours is shifted to off-peak hours.
Based on the above three ways, the demand response scheme is formulated as follows:
the first type is: the demand characteristics of the electric equipment are only determined by the environmental state and the operation state of the current time period, the adjacent time periods have no relation, and the response of the electric equipment under the real-time electricity price is as follows:
s.t.p(t)＝d(t) 0≤d0(t)-d(t)≤ΔD(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is the real-time electricity price and comfort loss factor of the corresponding terminal, d0(t) is the original user power demand, and Δ d (t) is the maximum value of the change in power demand.
The second type: the demand characteristics of the electric equipment are independent of time, the electric equipment is turned on at any time in a day and then turned off after completing work for a certain period, and the optimal response behaviors of the electric equipment are as follows:
s.t.u(t+s)＝1ifu(t)＝1(s＝1,2,…,τ)，
wherein d (T) is the electric energy requirement, λ (T) and μ (T) are the real-time electricity price and the comfort loss factor of the corresponding terminal, U (T) is the switching control quantity of the third type of equipment, and U is the sum of the switching control quantity at T moments; τ denotes the period length in h.
The third type: the demand characteristics of the electric equipment are determined not only by the environmental state and the operation state of the current time period, but also by the time coupling relation of the adjacent time periods, and the optimal response behaviors of the electric equipment are as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is the real-time electricity price and the comfort loss factor of the corresponding terminal, V (t) is the energy level when the electric equipment is used, and v (t) represents the part representing the energy coupling of the t time period and the t +1 time period;
is the upper limit of the energy level of the consumer; v is the lower energy level limit of the consumer.
For the electric equipment i, discretizing the electric level of the equipment according to the equipment parameters, and taking the discretized electric level as a state, wherein each electric level has corresponding action;
assuming that the power consumption level of the electric equipment is just divided into H points, plus the shutdown state of the equipment, the operation state of the electric equipment is discretized into H +1 power consumption levels, and at time t, each power consumption level is taken as a state s (t) of the electric equipment, that is:
in the above formula, pi(t) represents the power utilization decision of the power utilization equipment i in the t period, and the unit is as follows: kW.h; p represents the lower limit of the user's electric power, unit: kW; h represents the H level of the divided H power utilization levels; Δ represents discretization with electrical level;
the state set s (t) { s) at time t0,s1,…,sH}，si(i-0, …, H) represents each state in the set of states;
when the state of the electric equipment is in s (t), the basis for the Agent to select the action to carry out the state transition is influenced by several factors: the current power price lambda level, the comfort requirement of the user and the influence of the electricity demand characteristics of the user; for the consumers of the second type, the energy level V of the device also needs to be taken into account.
Analyzing a large number of historical load curves under the same demand mode to realize the decomposition of the four types of load demands;
1) base load
Cluster center vector p to classify k0 kThe curve is regarded as a trend curve of the user base load, and meanwhile, according to the user background situation, the response participation coefficient gamma of the user demand side is determined, so that the base load curve is as follows:
in the formula,
representing the original electricity demand of the kth class;
2) transferable load
The transferable load means that the requirements of the electric equipment are irrelevant at the same time, and the electric equipment is started at any time in one day and then is closed after finishing the work for a certain period of time according to a certain fixed mode;
the key point of the transferable load decomposition is the analysis and identification of load peaks and transfer quantity, and for the load peaks, the peak quantity and the peak center moment are determined according to the local maximum value of the basic load trend curve of the classification k
The range of transfer times for which the load can be transferred is then:
wherein,
and
respectively representing the peak transition boundary values derived from the user data;
the transferable load levels were:
in the formula (d)s(t) represents the total load demand function,
The required power consumption amount at the time of the lower limit transition,
Indicating the power demand at the time of transferring the upper limit;
3) can reduce the load
The load can be reduced as the variable part of the load after the basic load and the transferable load are removed, and the calculation formula is as follows:
in the formula,
respectively a reducible load, a total load demand function, a transferable load level curve and a basic load curve;
4) storable load
The storable load depends on whether the user has a corresponding storage device, the relevant parameters being considered as input quantities.
Preferably, the load curve classification and parameter setting are utilized to realize the online simulation of the responses of different user demand sides based on the actual power consumption data of the user;
the online simulation takes the state transition of different types of loads as a basic strategy and takes the randomly generated demand response intensity as an input parameter to realize the demand response strategy simulation of an online given user actual curve;
the three types of demand response strategies are implemented as follows:
transferable load
Wherein,
is a random variable;
the expression parameter is
Uniform distribution of (2);
can reduce the load
Wherein psiSIn order to be able to reduce the random variation of the load strength,
it is shown that the load can be reduced,
is shown in
ΨCAs a function of the parameter;
storable load
The response model of the storable load is as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above description. Therefore, the appended claims should be construed to cover all such variations and modifications as fall within the true spirit and scope of the invention. Any and all equivalent ranges and contents within the scope of the claims should be considered to be within the intent and scope of the present invention.
Claims (1)
1. A user side demand response method under a power selling side discharge environment is characterized in that a clustering algorithm is utilized, a plurality of dimensions including power consumption load curve characteristics, power consumption habits and user experience levels are selected, a large number of user power consumption load curves are clustered, users with similar power consumption load curve characteristics are clustered in the same class, and the user load curves in different classes are obviously different;
dividing a large number of users through a clustering algorithm, and formulating a demand response strategy for the same class of users;
when a demand response strategy is formulated, a daily load curve of a user is regarded as consisting of a basic load, a transferable load, a reducible load and a storable load, and then the demand response strategy is formulated respectively for each component;
the clustering of the user electricity load curve is based on a K-MEANS algorithm, and comprises the steps of dividing n data objects into K clusters, so that the sum of squares from data points in each cluster to a cluster center is minimum, and the algorithm processing process is as follows:
inputting: a cluster number k, a data set containing n data objects; and (3) outputting: k clusters; the method comprises the following specific steps:
the first step is as follows: randomly selecting k objects from n data objects as initial clustering centers;
the second step is that: respectively calculating the distance from each data object to each cluster center, and distributing the data objects to the clusters with the closest distance;
the third step: after all the data objects are distributed, recalculating centers of the k clusters;
the fourth step: comparing with k clustering centers obtained by previous calculation, if the clustering centers change, turning to the second step, otherwise, turning to the fifth step;
the fifth step: outputting a clustering result;
the demand response scheme that the user responds to the dynamic electricity price by adjusting the own electricity consumption behavior comprises the following three modes:
first, responding to electricity rates in the form of reducing electricity usage during peak electricity usage hours;
second, transferring the electricity usage during peak hours to off-peak hours;
thirdly, not only reducing the electricity consumption in peak time, but also transferring the electricity consumption in peak time to off-peak time;
based on the above three ways, the demand response scheme is formulated as follows:
the first type is: the demand characteristics of the electric equipment are only determined by the environmental state and the operation state of the current time period, the adjacent time periods have no relation, and the response of the electric equipment under the real-time electricity price is as follows:
s.t.p(t)＝d(t)，0≤d0(t)-d(t)≤ΔD(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is the real-time electricity price and comfort loss factor of the corresponding terminal, d0(t) is the original user power demand, and Δ d (t) is the maximum value of the power demand variation; t represents an amount of time;
the second type: the demand characteristics of the electric equipment are independent of time, the electric equipment is turned on at any time in a day and then turned off after completing work for a certain period, and the optimal response behaviors of the electric equipment are as follows:
s.t.u(t+s)＝1，if u(t)＝1(s＝1,2,…,τ)，
wherein d (T) is the electric energy requirement, lambda (T) is the real-time electricity price, U (T) is the switching control quantity of the second type of equipment, and U is the sum of the switching control quantities at T moments; τ represents the period length in h;
the third type: the demand characteristics of the electric equipment are determined not only by the environmental state and the operation state of the current time period, but also by the time coupling relation of the adjacent time periods, and the optimal response behaviors of the electric equipment are as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
where p (t) and d (t) are consumption and demand of electrical energy, λ (t) and μ (d)0(t) -d (t) is a real-time electricity price and a comfort loss factor of a corresponding terminal, V (t) is an energy level when the electric equipment is used, and v (t) represents a part representing energy coupling of a t time period and a t +1 time period;
is the upper limit of the energy level of the consumer;Vis powered by electricityA device energy level lower limit;
for the electric equipment i, discretizing the electric level of the equipment according to the equipment parameters, and taking the discretized electric level as a state, wherein each electric level has corresponding action;
assuming that the power consumption level of the electric equipment is just divided into H points, plus the shutdown state of the equipment, the operation state of the electric equipment is discretized into H +1 power consumption levels, and at time t, each power consumption level is taken as a state s (t) of the electric equipment, that is:
in the above formula, pi(t) represents the power utilization decision of the power utilization equipment i in the t period, and the unit is as follows: kW.h;Prepresents the lower limit of the electric power used by the user, and the unit is: kW; h represents the H level of the divided H power utilization levels; Δ represents discretization with electrical level; 0<h≤H；
The state set s (t) { s) at time t0,s1,…,sH}，si(i-0, …, H) represents each state in the set of states;
when the state of the electric equipment is in s (t), the basis for the Agent to select the action to carry out the state transition is influenced by several factors: the current power price lambda level, the comfort requirement of the user and the influence of the electricity demand characteristics of the user; for the consumer of the second type, the energy level V of the device also needs to be taken into account;
analyzing a large number of historical load curves under the same demand mode to realize the decomposition of four types of load demands, namely basic load, transferable load, reducible load and storable load;
1) base load
Regarding the clustering center vector of the classification k as a trend curve of the user basic load, and simultaneously determining a response participation coefficient gamma of a user demand side according to the user background condition, so as to obtain the basic load curve
Comprises the following steps:
in the formula,
representing the original electricity demand of the kth class;
2) transferable load
The transferable load means that the requirements of the electric equipment capable of transferring the load are irrelevant at the same time, and the electric equipment is started at any time in one day and then closed after finishing working according to a fixed mode of running time;
the key point of the transferable load decomposition is the analysis and identification of load peaks and transfer quantity, and for the load peaks, the peak quantity and the peak center time T are determined according to the local maximum value of the basic load trend curve of the classification kk PThen the range of transfer times for which the load can be transferred is:
wherein, Deltak +And deltak －Respectively representing the peak transition boundary values derived from the user data;
the transferable load levels were:
in the formula (d)s(t) represents the total load demand function,
The required power consumption amount at the time of the lower limit transition,
Indicating the power demand at the time of transferring the upper limit;
3) can reduce the load
The load can be reduced as the variable part of the load after the basic load and the transferable load are removed, and the calculation formula is as follows:
in the formula,
ds(t)、
respectively a reducible load, a total load demand function, a transferable load level curve and a basic load curve;
4) storable load
The storable load depends on whether the user has corresponding storage equipment or not, and relevant parameters are regarded as input quantity;
by utilizing 2) transferable load, 3) reducible load and 4) storable load to the classification of load curve and parameter setting thereof, the online simulation of different user demand side responses is realized based on the actual power consumption data of users;
the online simulation takes the state transition of different types of loads as a basic strategy and takes the randomly generated demand response intensity as an input parameter to realize the demand response strategy simulation of an online given user actual curve;
the three types of demand response strategies are implemented as follows:
transferable load
Wherein,
is a random variable;
the expression parameter is
Uniform distribution of (2);
can reduce the load
Wherein psiSIn order to be able to reduce the random variation of the load strength,
it is shown that the load can be reduced,
is shown in
ΨSAs a function of the parameter;
storable load
The response model of the storable load is as follows:
s.t.V(t)＝V(t-1)+p(t)-d(t)-ν(t)，
0≤d0(t)-d(t)≤ΔD(t)。
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