CN114825371B - Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation - Google Patents
Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation Download PDFInfo
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Abstract
The invention discloses an aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation, which belongs to the technical field of aggregation temperature control load multi-layer regulation and control strategies and comprises a power grid dispatching layer optimization stage, an aggregator distribution layer optimization stage and a load control layer optimization stage; according to the scheme, an optimized distribution method is introduced into a temperature control load multilayer regulation strategy model, a three-layer optimized regulation model is established, a specific regulation scheme of the air conditioner load is solved, the influence of regulation behaviors on a power grid is considered during optimized distribution, the voltage deviation of nodes before and after regulation is used as optimization constraint, the power grid bus loss is used as an optimization target, the safe and stable operation of the power grid is guaranteed, and meanwhile, the power grid loss is reduced.
Description
Technical Field
The invention belongs to the technical field of aggregation temperature control load multilayer regulation strategies, and particularly relates to an aggregation temperature control load multilayer regulation method based on node voltage constraints before and after regulation.
Background
With the introduction of the "dual carbon" target, new energy power generation technology has been greatly developed in recent years as an important technology for electric power systems to promote the "dual carbon" target. However, due to the randomness and the fluctuation of new energy power generation and the increasingly large load side, the safe and stable operation of the power grid faces new challenges. The traditional power grid regulation and control mode cannot meet the requirements of a novel power system, and the power grid needs to be regulated and controlled through a load side means urgently. In the load-side adjustable resource, due to the temperature-controlled load, for example: air conditioner load, heat pump, electric water heater, refrigerator, etc. can respond to control signal fast, and have the characteristics of large scale and wide distribution, often regard as the important development object of participating in the electric wire netting regulation and control.
The existing relevant research on the aggregation temperature control load multi-layer regulation strategy does not consider the influence of temperature control load regulation behavior on the operation of a power grid; in addition, when the load aggregators participate in scheduling, the adjustment amount is distributed in a market mode, so that part of the aggregators cannot participate in the adjustment action, the positivity of the aggregators is eliminated, the adjustment capacity of part of the aggregators can be completely applied by adopting a market mechanism, the adjustment capacity of part of the aggregators cannot be completely applied, and the adjustment action may cause overlarge deviation before and after voltage adjustment of certain nodes of the power grid, so that certain influence is caused on the safety and stability of the power grid.
Disclosure of Invention
Aiming at the defects in the prior art, the aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation introduces a distribution mechanism, establishes a three-layer optimization scheduling strategy model, solves the model by using an intelligent optimization algorithm, ensures safe and stable operation and user comfort of a power grid, and solves the problems of unfairness and unstable power grid safety caused by regulation behavior possibly caused by market mechanism when the aggregation temperature control load participates in power grid scheduling.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the invention provides an aggregation temperature control load multilayer regulation and control method based on node voltage constraints before and after adjustment, which comprises a power grid dispatching layer optimization stage, an aggregation provider distribution layer optimization stage and a load control layer optimization stage:
the power grid dispatching layer optimizing stage comprises the following steps:
s1, quantifying the temperature control load regulation capacity to obtain a load aggregation quotient adjustable capacity interval;
s2, obtaining the total adjustment quantity of the load aggregation trader based on the adjustable capacity interval of the load aggregation trader;
the aggregator distribution layer optimization stage comprises the following steps:
a1, calculating to obtain the power grid bus loss based on the total adjustment quantity of the load aggregation quotient;
a2, calculating to obtain a aggregator distribution target model based on the power grid bus loss;
a3, distributing a target model based on the aggregator, and obtaining the adjustment quantity of a single aggregator by using a multi-dimensional PSO method;
the load control layer optimization stage comprises the following steps:
b1, fitting the discomfort level of the user and the indoor temperature by using an interpolation method based on the adjustment quantity of the single aggregation quotient to obtain a comfort model of the user;
b2, obtaining a user comfort optimization target model based on the user comfort model;
and B3, calculating a user comfort optimization target model by using a particle swarm optimization algorithm to obtain the temperature regulating quantity of each user, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraints before and after regulation.
The invention has the beneficial effects that: in order to better stimulate a load aggregator to participate in regulation and control enthusiasm, the aggregation temperature control load multi-layer regulation and control method introduces an optimization distribution method into a temperature control load multi-layer regulation and control strategy model, establishes a three-layer optimization regulation and control model, solves a specific regulation scheme of the air conditioner load, considers the influence of regulation behaviors on a power grid during optimization distribution, takes the voltage deviation of the nodes before and after regulation as optimization constraint, and takes the power grid bus loss as an optimization target, thereby ensuring the safe and stable operation of the power grid and reducing the power grid loss.
Further, the step S1 includes the steps of:
s11, constructing a second-order equivalent thermal parameter model of a single air conditioner load:
wherein the content of the first and second substances,
denotes the cooling rate of the indoor temperature at time T, T i (t) represents the room temperature at time t, C a Represents the indoor equivalent capacitance, R 2 Represents the equivalent thermal resistance, R, of indoor air and wall 1 Denotes the equivalent thermal resistance, T, of indoor air and outdoor m (T) building wall temperature at time T, T o (t) represents the outdoor temperature at time t, Q (t) represents the heat exchange amount between the air conditioning load and the indoor air at time t, and/or the indoor temperature>
Temperature cooling rate of building wall body C at time t m Representing the equivalent heat capacity of the wall;
s12, constructing start-stop parameters of the air-conditioning load based on a second-order equivalent thermal parameter model of the single air-conditioning load:
wherein s (T + 1) represents an air-conditioning load start-stop parameter at the moment of T +1, T set The temperature setting value is represented, delta is represented by a temperature dead zone, s (t) is represented by an air conditioning load start-stop parameter at the moment t, and otherwise is represented by other conditions;
s13, obtaining an indoor temperature model when the air-conditioning load is closed, an indoor temperature model when the air-conditioning load is opened and a power model based on a second-order equivalent thermal parameter model of a single air-conditioning load and a start-stop parameter model of the air-conditioning load;
the indoor temperature model when the air conditioner load is closed, the indoor temperature model when the air conditioner load is opened and the power model expressions are respectively as follows:
wherein T represents the indoor temperature at time T +1,
denotes the outdoor temperature, T, at time T +1 i t Denotes the indoor temperature at time t, e denotes a natural base number, Δ t denotes a time interval, and Q denotes the heat exchange between the air conditioning load and the indoor spaceThe conversion quantity is that R represents indoor equivalent thermal resistance, P represents electric power of air conditioning load, and eta represents energy efficiency ratio;
s14, calculating to obtain air conditioner load aggregated power P based on start-stop parameters and power model of air conditioner load agg ：
Wherein N represents the total air conditioning load, P i Electric power representing the ith air-conditioning load, s (i) representing a start-stop parameter of the ith air-conditioning load;
s15, based on the indoor temperature model when the air-conditioning load is closed, the indoor temperature model when the air-conditioning load is opened, the starting and stopping parameters of the air-conditioning load and the air-conditioning load aggregated power P agg To obtain the air-conditioning load aggregate quotient power P aggi ：
P aggi ＝P aggi (R,C a ,T o ,T set )
Wherein, P aggi (. Cndot.) represents an air conditioning load aggregator aggregate power function;
s16, aggregating power P based on air conditioner load aggregation quotient aggi And obtaining a load aggregation quotient adjustable capacity interval:
[P dmin,aggi ,P dmax,aggi ]＝[0,P aggi (R,C a ,T o ,T set )-P aggi (R,C a ,T o ,T set +T * )]
wherein, P dmin,aggi Represents the lowest air conditioning load aggregate, P dmax,aggi Represents the highest air conditioning load aggregate, T * Indicating the amount of change in the air conditioning load setting temperature that is acceptable to the user.
The beneficial effect of adopting the above further scheme is that: and providing a calculation method of the load aggregation provider adjustable capacity interval, and providing a basis for calculating and obtaining the total adjustment quantity of the load aggregation provider.
Further, the step S2 includes the steps of:
s21, constructing a minimum power grid load peak value model based on the load aggregation quotient adjustable capacity interval:
min(max(P base,i′ +P ac,i′ ))
P base,i′ +P ac,i′ ＝P grid,i′ +P re,i′
wherein, P base,i′ Indicating loads other than the air-conditioning load at the i' th moment, P ac,i′ Represents the total air conditioning load at time i', P grid,i′ Representing the grid power consumption at time i', P re,i′ Representing the power provided by the new energy source at the ith' moment;
s22, constructing a real-time electricity price and load total linear model of the market at the demand side:
P dr ＝aP all +b
wherein, P dr Representing the real-time electricity prices of the demand-side market, a representing the first positive electricity price coefficient, P all Representing the total load of the power grid, and b representing a second positive valence coefficient;
s23, constructing a relation model of subsidy of the load aggregator to the user and response reduction of the demand side based on a real-time electricity price and load total linear model of the demand side market:
B＝k·P dr 2 ＝k(L before -L) 2
L＝L base +L ac
wherein B represents the subsidy of the load aggregator to the user, k represents the subsidy cost coefficient of the aggregator to the user, and L before Indicating the total load before the demand side responds, L indicating the total load after the demand side responds, L base Representing the grid base load, L ac Representing an air conditioning load;
s24, obtaining a profit model of the load aggregator based on a relation model of subsidy of the load aggregator to the user and reduction quantity response of the demand side:
wherein G represents the revenue of the load aggregator, P all,t Representtotal grid load at time t, t 1 Denotes t 1 Time of day t 2 Represents t 2 Time of day, P dr,t Representing the real-time electricity prices of the demand-side market at time t;
s25, obtaining an optimization target model of a power grid dispatching layer based on a profit model of the load aggregator:
P dr,t,min ≤P dr,t ≤P dr,t,max
wherein, P dr,t,min Minimum value, P, representing real-time electricity prices for demand-side markets at time t dr,t,max The maximum value of the real-time electricity price of the demand side market at the moment t is represented;
and S26, solving the power grid dispatching layer optimization target model by using a multi-target genetic algorithm NSGA-II to obtain the total regulating quantity of the load aggregators.
The beneficial effect of adopting the further scheme is as follows: and providing a calculation method of the total adjustment quantity of the load aggregation provider, and providing the adjustment quantity information of the aggregation provider for the optimization of the distribution layer of the aggregation provider.
Further, the step A1 includes the steps of:
a11, calculating the load flow of each power transmission line in the power grid based on the total adjustment quantity of the load aggregation quotient;
the load flow calculation expression of each power transmission line in the power grid is as follows:
v m -v n ＝2(r mn P mn +x mn Q mn )-(r mn 2 +x mn 2 )l mn
l mn v mn ＝P mn 2 +Q mn 2
l mn ≥0
wherein v is m And v n Respectively representing the squares, r, of the voltage amplitudes of the transmission line node m and the transmission line node n mn And x mn Respectively representing the resistance and reactance, P, of the mn section of the transmission line mn And Q mn Respectively representing the active power and the reactive power at the node m side of the transmission line, l mn Representing the square of the current amplitude of the mn section of the transmission line;
a12, calculating to obtain the power grid bus loss based on the power flow of each power transmission line in the power grid:
wherein, P loss Represents the loss of the power grid bus l i″j And x i″j Respectively represent the square sum reactance of the current amplitude of i 'j sections of the power transmission line, i' represents the ith 'node of the power transmission line, j represents the jth node of the power transmission line, and n' represents the total number of the nodes of the power transmission line.
The beneficial effect of adopting the further scheme is as follows: and a power grid bus loss calculation method is provided, and a basis is provided for obtaining a aggregator distribution target model.
Further, the expression of the aggregator assigning target model in the step A2 is as follows:
wherein vdiff i″ Indicates the voltage offset, v, before and after the i' th node adjustment i″,t Indicating the regulated node voltage, v, at the ith "node i″,o Denotes the node voltage before the i "th node is regulated, v set1 And v set2 Respectively representing a first preset voltage offset and a second preset voltage offset.
The beneficial effect of adopting the further scheme is as follows: and providing a calculation method of the aggregator allocation target model, and providing a basis for obtaining the adjustment quantity of the single aggregator.
Further, the expression of the comfort model of the user in step B1 is as follows:
wherein PDD represents dissatisfaction of a user with indoor temperature, a' represents a direct proportionality coefficient,
indicates a temperature setpoint after the adjustment>
Indicating the user's desired temperature.
The beneficial effect of adopting the further scheme is as follows: and providing a comfort degree model calculation method for the user, and providing a basis for obtaining a user comfort degree optimization target model.
Further, the step B2 includes the steps of:
b21, obtaining a single user discomfort level χ based on the comfort model of the user:
b22, obtaining a user comfort optimization target model based on the single user discomfort level χ:
R new ≥R newset
wherein the content of the first and second substances,
indicating preset body feelingComfortable ambient temperature variation threshold, R new The curve fitting rate R between the daily load curve and the optimal daily load curve after the variation threshold of the somatosensory comfortable external temperature is changed newset Represents a predetermined curve fitting ratio, y 1,t Represents the actual load at time t, y t Indicating the target load at time t.
The beneficial effect of adopting the further scheme is as follows: and providing a calculation method of the user comfort optimization objective model, and providing a basis for obtaining the temperature regulation quantity of each user.
Further, the step B3 includes the steps of:
b31, defining the particle swarm optimization variable quantity to be the same as the total number of users in the user comfort optimization target model, and obtaining the positions of the particles:
g(t+1)＝c 1 r 1 (t)(p i″′ (t)-x i″′ (t))+c 2 r 2 (t)(p g (t)-x i″′ (t))
wherein x (t + 1) represents the particle position at time t + 1, x (t) represents the particle position at time t, g (t + 1) represents the particle direction vector at time t + 1, sig (c) represents the random movement of the particle in the positive and negative directions of each dimension, c 1 And c 2 Respectively representing a first learning factor and a second learning factor, r 1 (t) and r 2 (t) first and second random numbers representing time t, x i″′ (t) represents the position of the i' th particle at time t, p i″′ (t) represents the optimal position on all the paths traveled by the i' th particle at time t, p g (t) represents the optimal position on the way all particles have traveled, c represents the c-th particle, where c is subject to a uniform distribution;
the beneficial effect of adopting the above further scheme is that: and providing a calculation method for solving the particle positions in the user comfort optimization target model by using a particle swarm algorithm, and providing a basis for obtaining the temperature regulation quantity of each user.
And further, based on the particle positions, obtaining the temperature regulating quantity of each user, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraints before and after regulation.
The beneficial effect of adopting the further scheme is as follows: and the temperature regulating quantity of each user is obtained through the particle position, so that the multi-layer regulation and control of the polymerization temperature control load are realized.
Drawings
Fig. 1 is a flowchart illustrating steps of an aggregated temperature-controlled load multi-layer regulation method based on node voltage constraints before and after regulation according to an embodiment of the present invention.
Fig. 2 is a second-order equivalent thermal parameter model of a single air conditioning load in an embodiment of the present invention.
Fig. 3 is a network topology diagram of a multi-layer regulation and control strategy simulation for aggregating temperature control loads based on an IEEE33 node distribution network system in the embodiment of the present invention.
Fig. 4 is a diagram illustrating a new energy output and load condition prediction at an air conditioning load control point in the embodiment of the present invention.
FIG. 5 is a diagram illustrating temperature variation prediction according to an embodiment of the present invention.
Fig. 6 is a pareto curve targeted for load peak minimization and load aggregator maximum benefit in an embodiment of the invention.
FIG. 7 is a daily load graph in an embodiment of the present invention.
Fig. 8 is a schematic diagram of the convergence process of the optimization algorithm in the optimization stage of the distribution layer in the embodiment of the present invention.
Fig. 9 is an air conditioning load curve of the load aggregator 1 in the embodiment of the present invention.
FIG. 10 is a graph showing the change in temperature of the load aggregator 1 in the example of the present invention.
Fig. 11 is a subsidy graph obtained by each user administered by the load aggregator 1 in the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1
As shown in fig. 1, in an embodiment of the present invention, the present invention provides an aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation, including a power grid scheduling layer optimization stage, an aggregator distribution layer optimization stage, and a load control layer optimization stage:
the power grid dispatching layer optimizing stage comprises the following steps:
s1, quantifying the temperature control load regulation capacity to obtain a load aggregation quotient adjustable capacity interval;
the step S1 includes the steps of:
as shown in fig. 2, for a single air conditioning load model, an Equivalent Thermal Parameter (ETP) model is often described, which is a process of exchanging energy between a space where the air conditioning load is located and the outside, and is described by using a circuit including an equivalent thermal resistance and an equivalent thermal capacitance, and this model is often suitable for modeling an air conditioning model for a home user.
S11, constructing a second-order equivalent thermal parameter model of a single air conditioner load:
wherein the content of the first and second substances,
denotes the cooling rate of the indoor temperature at time T, T i (t) indoor temperature at time tDegree C a Represents the indoor equivalent capacitance, R 2 Represents the equivalent thermal resistance, R, of indoor air and wall 1 Denotes the equivalent thermal resistance, T, of indoor air and outdoor m (T) building wall temperature at time T, T o (t) represents the outdoor temperature at time t, Q (t) represents the heat exchange amount between the air conditioning load and the indoor air at time t, and/or the indoor temperature>
Temperature cooling rate of building wall body C at time t m Representing the equivalent heat capacity of the wall;
s12, constructing start-stop parameters of the air-conditioning load based on a second-order equivalent thermal parameter model of the single air-conditioning load:
wherein s (T + 1) represents the air-conditioning load start-stop parameter at the moment of T +1, T set The temperature of the air conditioner load is set, delta represents a temperature dead zone, s (t) represents an air conditioner load start-stop parameter at the moment t, otherwise represents other conditions, the air conditioner load start-stop parameter is equal to 1 to represent that the air conditioner load is started, and the air conditioner load start-stop parameter is equal to 0 to represent that the air conditioner load is stopped;
when the indoor temperature is higher than the maximum temperature limit value, the load is started; when the load is smaller than the minimum limit value, the load is closed;
s13, on the basis of a second-order equivalent thermal parameter model of a single air-conditioning load and a start-stop parameter model of the air-conditioning load, and under the condition that wall body parameters are ignored, an indoor temperature model when the air-conditioning load is closed, an indoor temperature model when the air-conditioning load is opened and a power model are obtained;
the indoor temperature model when the air conditioner load is closed, the indoor temperature model when the air conditioner load is opened and the power model expressions are respectively as follows:
wherein, T i t+1 Represents the indoor temperature at the time t + 1,
represents the outdoor temperature at time T + 1, T i t Expressing the indoor temperature at the time t, e expressing a natural base number, delta t expressing a time interval, Q expressing the heat exchange quantity between the air conditioning load and the indoor space, R expressing the indoor equivalent thermal resistance, P expressing the electric power of the air conditioning load, and eta expressing the energy efficiency ratio;
s14, calculating to obtain air conditioner load aggregated power P based on start-stop parameters and power model of air conditioner load agg ：
Wherein N represents the total air conditioning load, P i Electric power representing the ith air-conditioning load, s (i) representing a start-stop parameter of the ith air-conditioning load;
s15, based on the indoor temperature model when the air-conditioning load is closed, the indoor temperature model when the air-conditioning load is opened, the starting and stopping parameters of the air-conditioning load and the air-conditioning load aggregated power P agg To obtain the air-conditioning load aggregate quotient power P aggi ：
P aggi ＝P aggi (R,C a ,T o ,T set )
Wherein, P aggi (. Cndot.) represents an air conditioning load aggregator aggregate power function;
s16, aggregating the power P based on the air conditioner load aggi And obtaining a load aggregation quotient adjustable capacity interval:
[P dmin,aggi ,P dmax,aggi ]＝[0,P aggi (R,C a ,T o ,T set )-P aggi (R,C a ,T o ,T set +T * )]
wherein, P dmin,aggi Represents the lowest air conditioning load aggregate quotient, P dmax,aggi Represents the highest air conditioning load aggregate, T * Indicating an air conditioning load setting temperature change amount acceptable to a user;
the power grid often pays attention to the stability and safety of operation; aggregators often concern themselves with the benefits available; the consumption of the new energy output is also a problem to be paid attention to, so in a scheduling layer, on the premise that the new energy is completely consumed, an optimization model is established by considering the load peak value of a power grid and the benefits of a aggregator; the peak value of the power grid is an important factor influencing the safe and stable operation of the power grid, and the excessive peak value of the power grid may bring irreversible damage to some equipment of the power system, so the peak value of the load of the power grid is often used as one of the consideration factors for the optimal scheduling of the power system;
s2, obtaining the total adjustment quantity of the load aggregation trader based on the adjustable capacity interval of the load aggregation trader;
the step S2 includes the steps of:
s21, constructing a minimum power grid load peak value model based on the load aggregation quotient adjustable capacity interval:
min(max(P base,i′ +P ac,i′ ))
P base,i′ +P ac,i′ ＝P grid,i′ +P re,i′
wherein, P base,i′ Indicating loads other than the air-conditioning load, P, at the i' th moment ac,i′ Represents the total air conditioning load at time i', P grid,i′ Representing the power consumed by the grid at time i', P re,i′ Representing the power provided by the new energy source at the ith' moment;
in the power grid dispatching level, in order to improve the user enthusiasm, a load aggregator provides demand side response resources for the power grid, and the power grid pays certain cost; the user provides the adjustment service for the load aggregator, and the aggregator gives a certain subsidy to the user, so that the total benefit available to the load aggregator is used as another target of the scheduling layer;
the electricity price of the power grid company for the participating demand side response electricity quantity of the load aggregator is usually the real-time electricity price of the demand side response market, and the electricity price and the total load amount have a certain linear relation;
s22, constructing a real-time electricity price and load total linear model of the market at the demand side:
P dr ＝aP all +b
wherein, P dr Representing the real-time electricity prices of the demand-side market, a represents the first positive price coefficient, P all Representing the total load of the power grid, and b representing a second positive electricity price coefficient, wherein when the total load is increased, the corresponding real-time electricity price is also increased;
s23, constructing a relation model of subsidy of the load aggregator to the user and response reduction of the demand side based on a real-time electricity price and load total linear model of the demand side market:
B＝k·P dr 2 ＝k(L before -L) 2
L＝L base +L ac
wherein B represents subsidy of the load aggregator to the user, k represents subsidy cost coefficient of the aggregator to the user, and L before Indicating the total load before the demand side responds, L indicating the total load after the demand side responds, L base Representing the grid base load, L ac Represents the air conditioning load;
s24, obtaining a profit model of the load aggregator based on a relation model of subsidy of the load aggregator to the user and reduction quantity response of the demand side:
wherein G represents the revenue of the load aggregator, P all,t Representing the total grid load at time t, t 1 Represents t 1 Time of day, t 2 Denotes t 2 Time of day, P dr,t Representing the real-time electricity prices of the demand-side market at time t;
s25, obtaining an optimized target model of a power grid dispatching layer based on a profit model of the load aggregator:
P dr,t,min ≤P dr,t ≤P dr,t,max
wherein, P dr,t,min Minimum value, P, representing real-time electricity prices of demand-side market at time t dr,t,max The maximum value of the real-time electricity price of the demand side market at the moment t is represented;
s26, solving the power grid dispatching layer optimization target model by using a multi-target genetic algorithm NSGA-II to obtain the total regulating quantity and the total benefits of the load aggregators;
after the adjustment quantity of the total load aggregators is obtained, the adjustment quantity is distributed, and during distribution, because the air conditioner load which can be regulated and controlled by each load aggregator has certain spatial difference, the influence of regulation and control behaviors on the operation of a power grid is fully considered during distribution;
because the transmission line has resistance, certain line loss exists in the transmission process, the line loss of the system is taken as an important parameter for representing the operation efficiency of the power grid, and the line loss is too large, which represents that the loss of the power grid is large in the transmission process;
according to the scheme, the line loss of the power grid is used as an optimization target of a distribution layer, and the system bus loss is calculated through load flow calculation.
The aggregator distribution layer optimization stage comprises the following steps:
a1, calculating to obtain the power grid bus loss based on the total adjustment quantity of the load aggregation quotient;
the step A1 comprises the following steps:
a11, calculating the load flow of each power transmission line in the power grid based on the total adjustment quantity of the load aggregation quotient;
the load flow calculation expression of each power transmission line in the power grid is as follows:
v m -v n ＝2(r mn P mn +x mn Q mn )-(r mn 2 +x mn 2 )l mn
l mn v mn ＝P mn 2 +Q mn 2
l mn ≥0
wherein v is m And v n Representing the square of the voltage amplitudes of transmission line node m and transmission line node n, r, respectively mn And x mn Respectively representing the resistance and reactance of the mn section of the transmission line, P mn And Q mn Respectively representing the active power and the reactive power at the node m side of the transmission line, l mn Representing the square of the current amplitude of the mn section of the transmission line;
a12, calculating to obtain the power grid bus loss based on the load flow of each power transmission line in the power grid:
wherein, P loss Represents the loss of the power grid bus l i″j And x i″j Respectively representing the square sum reactance of the current amplitude of i 'j sections of the power transmission line, i' representing the ith 'node of the power transmission line, j representing the jth node of the power transmission line, and n' representing the total number of the nodes of the power transmission line;
voltage is an important index for measuring the stability of a power grid, so in the regulation and control process, the change of node voltage also needs to be considered;
a2, calculating to obtain a aggregator distribution target model based on the power grid bus loss;
the expression of the aggregator allocation target model in the step A2 is as follows:
wherein vdiff i″ Indicates the voltage offset before and after the i "th node adjustment, v i″,t Indicating the regulated node voltage, v, at the ith "node i″,o Indicates the node voltage before the i' th node is regulated, v set1 And v set2 Respectively representing a first preset voltage offset and a second preset voltage offset, wherein the first preset voltage offset and the second preset voltage offset are usually 5, namely the voltage offset before and after the ith' node is adjusted is within 5 percent of increase or decrease;
a3, distributing a target model based on the aggregator, and obtaining the adjustment quantity of a single aggregator by using a multi-dimensional PSO method;
in the aspect of controlling the air conditioner load under the jurisdiction of a single load aggregator, from the perspective of a user, the comfort level of the user is optimized as the center of gravity; the thermal comfort model is a common model for quantifying the discomfort degree of the user and describing the dissatisfaction degree of the user to the room temperature, and because the discomfort degree is mainly related to the room temperature, other factors are assumed to be constants, and then the discomfort degree level of the user and the room temperature are fitted by utilizing an interpolation method;
the load control layer optimization stage comprises the following steps:
b1, fitting the discomfort level and the indoor temperature of the user by using an interpolation method based on the adjustment quantity of a single aggregator to obtain a comfort model of the user;
the expression of the comfort model of the user in step B1 is as follows:
wherein PDD represents dissatisfaction degree of user to indoor temperature, a' represents proportional coefficient,
represents the adjusted temperature setpoint>
Indicating a user temperature desired value;
b2, obtaining a user comfort optimization target model based on the user comfort model;
the step B2 comprises the following steps:
b21, obtaining a single user discomfort level χ based on the comfort model of the user:
when the optimization is carried out, the aggregation power constraint of the air conditioner load is also considered after the temperature set value is changed, and because the air conditioner load managed by the load aggregator has heterogeneity, the daily load curve obtained by temperature control is not completely consistent with the optimal daily load curve obtained by the aggregator, so that the fitting rate between the daily load curve after the temperature set value is changed and the optimal daily load curve at the upper layer is used as the power constraint of the user-level optimization model
B22, obtaining a user comfort optimization target model based on the single user discomfort level χ:
R new ≥R newset
wherein, T set* Indicating a preset somatosensory comfortable external temperature change threshold, R new Shows the curve fitting rate R between the daily load curve and the optimal daily load curve after the somatosensory comfortable external temperature change threshold value is changed newset Representing a predetermined curve fitting ratio, y 1,t Representing the actual load at time t, y t The target load at the time t is represented, wherein the preset curve fitting rate is usually 3, and when the external temperature changes by more than 3 ℃, a human body can obviously feel uncomfortable;
b3, calculating a user comfort optimization target model by utilizing a particle swarm algorithm to obtain the temperature regulating quantity of each user, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraints before and after regulation;
the particle swarm optimization is often used for solving the optimization problem with a large number of optimized individuals due to the characteristics of high calculation speed, clear algorithm logic and the like, the adjustment behaviors of users present diversity, the set temperature adjustment quantity of the current air conditioning load is an integer, and the specific temperature adjustment quantity of each user needs to be obtained;
the step B3 comprises the following steps:
b31, defining the particle swarm optimization variable quantity to be the same as the total number of users in the user comfort optimization target model, and obtaining the positions of the particles:
g(t+1)＝c 1 r 1 (t)(p i″′ (t)-x i″′ (t))+c 2 r 2 (t)(p g (t)-x i″′ (t))
wherein x (t + 1) represents the particle position at time t + 1, x (t) represents the particle position at time t, g (t + 1) represents the particle direction vector at time t + 1, sig (c) represents the random movement of the particle in the positive and negative directions of each dimension, and c 1 And c 2 Respectively representing a first learning factor and a second learning factor, r 1 (t) and r 2 (t) first and second random numbers representing time t, x i″′ (t) represents the position of the i' th particle at time t, p i″′ (t) represents the th of time tOptimal position, p, over all the runs that i' ″ particles have traveled g (t) represents the optimal position on the way all particles have traveled, c represents the c-th particle, where c is subject to a uniform distribution;
and B32, obtaining the temperature regulating quantity of each user based on the particle position, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraint before and after regulation.
Example 2
As shown in fig. 3, in a practical example of the present invention, the present invention performs aggregation temperature control load multi-layer regulation and control strategy simulation based on an IEEE33 node distribution network system, and on the basis of an IEEE33 node distribution network, as shown in a network topology diagram, it is assumed that there are air conditioning load aggregators in three areas in the diagram, and the air conditioning load regulation and control points of the air conditioning load aggregators are shown in the diagram, where there are not only the original constant and unchangeable load capacity of the network, but also the base load that changes with time and the air conditioning load that is regulated and controlled; and certain new energy is accessed in the new energy access points marked in the figure; the new energy output and the load (except the air conditioning load) of the air conditioning load control point are predicted on the next day as shown in fig. 4, and the temperature change prediction on the next day is shown in fig. 5;
on the basis of temperature change, each load aggregator manages 1000 air conditioning loads, and the distribution of load-related parameters is shown in table 1:
TABLE 1
Respectively simulating 1000 groups of data under each polymer quotient by using Monte Carlo; the maximum adjustment capacity (set value change of 3 ℃) of each load aggregator was quantified on the basis of the temperature change, and the adjustment capacities at each time were as follows:
the maximum adjustment capacity of the load aggregator 1 at each time is shown in table 2:
TABLE 2
The maximum adjustment capacity at each time of the load aggregator 2 is shown in table 3:
TABLE 3
The maximum adjustment capacity of the load aggregator 3 at each time is shown in table 4:
TABLE 4
And a scheduling layer optimization stage:
the new energy, the power grid and the total load aggregator form a scheduling layer, a pareto curve is obtained by utilizing an NSGA-II multi-objective optimization algorithm according to the goals of minimum load peak value and maximum benefit of the load aggregator, as shown in FIG. 6, wherein the abscissa represents the total benefit of the load aggregator, the ordinate represents the load peak value, the load peak value is considered to be minimum preferentially, an optimization result is selected, the daily load curve is obtained as shown in FIG. 7, the load peak value is 14406.061kW, the load aggregator provides peak regulation capacity of 1457.283kW for the power grid, 12241kW.h electricity is saved, the aggregator obtains benefit of 7588.988 elements, and a user subsidies 11386.5128 elements.
The total load aggregator adjustment at this time is shown in table 5:
TABLE 5
Distribution layer optimization stage:
after the total regulating quantity of the load aggregation businessmen at each moment is obtained, the distribution condition of the load aggregation businessmen on the node system network topology is considered, the minimum system network loss is taken as a target, the voltage deviation of each node before and after regulation is ensured to be between-5% and 5%, and the regulating quantity of each load aggregation businessmen is obtained by utilizing a multi-dimensional particle swarm optimization algorithm.
The optimization algorithm convergence process is shown in fig. 8, and the adjustment amount of each aggregator is shown in table 6:
TABLE 6
After the adjustment amount of the aggregator is obtained, the profit of the aggregator is distributed according to the total adjustment amount of each aggregator in one day, and the distribution result and the profit of each aggregator are shown in table 7 and table 8 respectively:
TABLE 7
| |
|
|
|
| Total regulating variable/kW.h | 4799.0031 | 4983.7824 | 3847.5309 |
TABLE 8
| |
|
|
|
| Benefit/element | 2671.9539 | 2774.8340 | 2142.2001 |
And a control layer optimization stage:
as shown in fig. 9 and 10, after the adjustment amount of each load aggregator is obtained, the adjustment and control are performed by changing the set value of the air conditioner load temperature, taking the optimum user comfort level as the optimization target, and the optimization solution is performed by using the particle swarm integer programming algorithm, so that the two-curve fitting rate of the load aggregator 1 (AGG 1) is 0.9124, the user comfort level is 2363, the two-curve fitting rate of the load aggregator 2 (AGG 2) is 0.9008, the user comfort level is 2622, the two-curve fitting rate of the load aggregator 3 (AGG 3) is 0.9149, and the user comfort level is 2280;
because there is a certain error between the actual load curve of the air conditioner load and the expected load curve, the error is reduced by using the energy storage configured by each load aggregator, the user subsidy needs to reduce the cost of the energy storage output (0.2 yuan/kw.h), and the obtained subsidy of the user and the obtained benefit of the load aggregator are respectively shown in table 9 and table 10:
TABLE 9
| User subsidy/element | Energy storage cost/dollar | Actual user subsidy/element |
| 11386.5128 | 665.5276 | 10720.9852 |
| |
|
|
|
| Air conditioning benefits/benefits | 2671.9539 | 2774.8340 | 2142.2001 |
| Energy storage output benefits/benefits | 279.8827 | 276.6066 | 109.0383 |
| Total benefit/element | 2951.8366 | 3051.4406 | 2251.2384 |
As shown in fig. 11, the contribution rate of a single user in the adjustment behavior is quantified: and for a single user, considering 1000 users with the same parameters, calculating the average adjusting capacity at each moment in the whole adjusting time period under each temperature change amount, and then distributing user subsidies according to the temperature change amount obtained by the control layer optimization result.
Claims (1)
1. An aggregation temperature control load multilayer regulation and control method based on node voltage constraints before and after adjustment is characterized by comprising a power grid dispatching layer optimization stage, an aggregator distribution layer optimization stage and a load control layer optimization stage:
the power grid dispatching layer optimizing stage comprises the following steps:
s1, quantifying the temperature control load regulation capacity to obtain a load aggregation quotient adjustable capacity interval;
s2, obtaining the total adjustment quantity of the load aggregation trader based on the adjustable capacity interval of the load aggregation trader;
the aggregator distribution layer optimization stage comprises the following steps:
a1, calculating to obtain the power grid bus loss based on the total adjustment quantity of the load aggregation quotient;
a2, calculating to obtain a aggregator distribution target model based on the power grid bus loss;
a3, distributing a target model based on the aggregator, and obtaining a single aggregator adjustment quantity by using a multi-dimensional PSO method;
the load control layer optimization stage comprises the following steps:
b1, fitting the discomfort level of the user and the indoor temperature by using an interpolation method based on the adjustment quantity of the single aggregation quotient to obtain a comfort model of the user;
b2, obtaining a user comfort optimization target model based on the user comfort model;
b3, calculating a user comfort optimization target model by using a particle swarm optimization algorithm to obtain the temperature regulating quantity of each user, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraints before and after regulation;
the step S1 includes the steps of:
s11, constructing a second-order equivalent thermal parameter model of a single air conditioner load:
wherein the content of the first and second substances,
denotes the cooling rate of the indoor temperature at time T, T i (t) represents the room temperature at time t, C a Represents the indoor equivalent capacitance, R 2 Represents the equivalent thermal resistance, R, of indoor air and wall 1 Denotes the equivalent thermal resistance, T, of indoor air and outdoor m (T) building wall temperature at time T, T o (t) represents the outdoor temperature at time t, Q (t) represents the heat exchange amount between the air conditioning load and the indoor air at time t, and/or the indoor temperature>
Temperature cooling rate of building wall body C at time t m Representing the equivalent heat capacity of the wall;
s12, constructing start-stop parameters of the air-conditioning load based on a second-order equivalent thermal parameter model of the single air-conditioning load:
wherein the content of the first and second substances,s (T + 1) represents the air-conditioning load start-stop parameter at the time of T +1, T set The temperature setting value is represented, delta is represented by a temperature dead zone, s (t) is represented by an air conditioning load start-stop parameter at the moment t, and otherwise is represented by other conditions;
s13, obtaining an indoor temperature model when the air-conditioning load is closed, an indoor temperature model when the air-conditioning load is opened and a power model based on a second-order equivalent thermal parameter model of a single air-conditioning load and a start-stop parameter model of the air-conditioning load;
the indoor temperature model when the air conditioner load is closed, the indoor temperature model when the air conditioner load is opened and the power model expressions are respectively as follows:
wherein, T i t+1 Represents the indoor temperature at time t +1,
denotes the outdoor temperature, T, at time T +1 i t The method comprises the following steps of (1) representing the indoor temperature at the moment t, e representing a natural base number, delta t representing a time interval, Q representing the heat exchange quantity between the air-conditioning load and the indoor space, R representing the indoor equivalent thermal resistance, P representing the electric power of the air-conditioning load, and eta representing the energy efficiency ratio;
s14, calculating to obtain air conditioner load aggregated power P based on start-stop parameters and power model of air conditioner load agg ：
Wherein N represents the total air conditioning load, P i An electric power representing an ith air conditioning load, s (i) a start-stop parameter representing the ith air conditioning load;
s15, based on the indoor temperature model when the air-conditioning load is closed, the indoor temperature model when the air-conditioning load is opened, the start-stop parameters of the air-conditioning load and the air-conditioning load aggregate power P agg To obtain the air conditioner load aggregate quotient aggregate power P aggi ：
P aggi ＝P aggi (R,C a ,T o ,T set )
Wherein, P aggi (. Cndot.) represents an air conditioning load aggregator aggregate power function;
s16, aggregating the power P based on the air conditioner load aggi And obtaining a load aggregation quotient adjustability interval:
[P dmin,aggi ,P dmax,aggi ]＝[0,P aggi (R,C a ,T o ,T set )-P aggi (R,C a ,T o ,T set +T * )]
wherein, P dmin,aggi Represents the lowest air conditioning load aggregate, P dmax,aggi Indicates the highest air conditioning load aggregate quotient, T * Indicating an air conditioning load setting temperature change amount acceptable to a user;
the step S2 includes the steps of:
s21, constructing a minimum power grid load peak value model based on the load aggregation quotient adjustable capacity interval:
min(max(P base,i′ +P ac,i′ ))
P base,i′ +P ac,i′ ＝P grid,i′ +P re,i′
wherein, P base,i′ Indicating loads other than the air-conditioning load at the i' th moment, P ac,i′ Represents the total air conditioning load at time i', P grid,i′ Representing the power consumed by the grid at time i', P re,i′ Representing the power provided by the new energy source at the ith' moment;
s22, constructing a real-time electricity price and load total linear model of the market at the demand side:
P dr ＝aP all +b
wherein, P dr Representing the real-time electricity prices of the demand-side market, a representing the first positive electricity price coefficient, P all Representing the total load of the power grid, and b representing a second positive valence coefficient;
s23, constructing a relation model of subsidy of the load aggregator to the user and response reduction of the demand side based on a real-time electricity price and load total linear model of the demand side market:
B＝k·P dr 2 ＝k(L before -L) 2
L＝L base +L ac
wherein B represents the subsidy of the load aggregator to the user, k represents the subsidy cost coefficient of the aggregator to the user, and L before Indicating the total load before the demand side response, L indicating the total load after the demand side response, L base Representing the grid base load, L ac Represents the air conditioning load;
s24, obtaining a profit model of the load aggregator based on a relation model of subsidy of the load aggregator to the user and reduction quantity response of the demand side:
wherein G represents the revenue of the load aggregator, P all,t Representing the total grid load at time t, t 1 Represents t 1 Time of day, t 2 Represents t 2 Time of day, P dr,t Representing the real-time electricity prices of the demand side market at time t;
s25, obtaining an optimized target model of a power grid dispatching layer based on a profit model of the load aggregator:
P dr,t,min ≤P dr,t ≤P dr,t,max
wherein, P dr,t,min Minimum value, P, representing real-time electricity prices of demand-side market at time t dr,t,max The maximum value of the real-time electricity price of the demand side market at the moment t is represented;
s26, solving the power grid dispatching layer optimization target model by using a multi-target genetic algorithm NSGA-II to obtain the total load aggregator adjustment quantity;
the step A1 comprises the following steps:
a11, calculating the load flow of each power transmission line in the power grid based on the total adjustment quantity of the load aggregation quotient;
the load flow calculation expression of each power transmission line in the power grid is as follows:
v m -v n ＝2(r mn P mn +x mn Q mn )-(r mn 2 +x mn 2 )l mn
l mn v mn ＝P mn 2 +Q mn 2
l mn ≥0
wherein v is m And v n Representing the square of the voltage amplitudes of transmission line node m and transmission line node n, r, respectively mn And x mn Respectively representing the resistance and reactance, P, of the mn section of the transmission line mn And Q mn Respectively representing the active power and the reactive power at the node m side of the transmission line, l mn Representing the square of the current amplitude, v, of the mn section of the transmission line mn Representing the square of the voltage amplitude of the mn section of the transmission line;
a12, calculating to obtain the power grid bus loss based on the power flow of each power transmission line in the power grid:
wherein, P loss Represents the loss of the power grid bus, l i″j And x i″j Respectively representing the square sum reactance of the current amplitude of i 'j section of the transmission line, i' representing the ith node of the transmission line, and j representing the current amplitude of the transmission lineThe jth node, n' represents the total number of nodes of the transmission line;
the expression of the aggregator allocation target model in the step A2 is as follows:
wherein vdiff i″ Indicates the voltage offset before and after the i "th node adjustment, v i″,t Indicating the regulated node voltage, v, at the ith "node i″,o Denotes the node voltage before the i "th node is regulated, v set1 And v set2 Respectively representing a first preset voltage offset and a second preset voltage offset;
the expression of the comfort model of the user in step B1 is as follows:
wherein PDD represents dissatisfaction of a user with indoor temperature, a' represents a direct proportionality coefficient,
indicates a temperature setpoint after the adjustment>
Indicating a user temperature desired value;
the step B2 comprises the following steps:
b21, obtaining the discomfort level χ of the single user based on the comfort model of the user:
b22, obtaining a user comfort optimization target model based on the single user discomfort level χ:
R new ≥R newset
wherein the content of the first and second substances,
represents a preset somatosensory comfortable external temperature change threshold value, R new The curve fitting rate R between the daily load curve and the optimal daily load curve after the variation threshold of the somatosensory comfortable external temperature is changed newset Represents a predetermined curve fitting ratio, y 1,t Representing the actual load at time t, y t Representing the target load at time t;
the step B3 comprises the following steps:
b31, defining the particle swarm optimization variable quantity to be the same as the total number of users in the user comfort optimization target model, and obtaining the positions of the particles:
g(t+1)＝c 1 r 1 (t)(p i″′ (t)-x i″′ (t))+c 2 r 2 (t)(p g (t)-x i″′ (t))
wherein x (t + 1) represents the particle position at time t +1, x (t) represents the particle position at time t, g (t + 1) represents the particle direction vector at time t +1, sig (c) represents the random movement of the particle in the positive and negative directions of each dimension, c 1 And c 2 Respectively representing a first learning factor and a second learning factor, r 1 (t) and r 2 (t) first and second random numbers representing time t, x i″′ (t) represents the position of the i' th particle at time t, p i″′ (t) represents the optimal position on all the paths traveled by the i' th particle at time t, p g (t) represents the optimal position on the way all particles have traveled, c represents the c-th particle, where c is subject to a uniform distribution;
and B32, obtaining the temperature regulating quantity of each user based on the particle position, and completing the aggregation temperature control load multi-layer regulation based on node voltage constraint before and after regulation.
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