CN108683174B - Network loss optimization method based on multi-time scale demand response model - Google Patents

Network loss optimization method based on multi-time scale demand response model Download PDF

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CN108683174B
CN108683174B CN201810352098.5A CN201810352098A CN108683174B CN 108683174 B CN108683174 B CN 108683174B CN 201810352098 A CN201810352098 A CN 201810352098A CN 108683174 B CN108683174 B CN 108683174B
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周丹
戴慧雯
任志伟
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Zhejiang University of Technology ZJUT
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Abstract

A network loss optimization method based on a multi-time scale demand response model is characterized by constructing the multi-time scale demand response model, initializing a system and acquiring original data; establishing a network loss optimization method model, and determining a target function, an optimization variable and a constraint condition; and finally, based on the programmed load flow calculation program, using a particle swarm algorithm as an algorithm of the network loss optimization model. According to the method, consumer psychology is considered in modeling, the long-term demand model and the short-term demand model are combined together through the demand elastic matrix, and a multi-time-scale demand side response model is established. The invention provides a new idea for a network loss optimization method and is beneficial to improving the economic efficiency of power enterprises.

Description

Network loss optimization method based on multi-time scale demand response model
Technical Field
The invention relates to a network loss optimization method for demand side response modeling.
Background
The new electricity changes 9 and proposes an electricity operation mode of 'control the middle and release the two ends', so that the demand side response has larger implementation space. The demand side response has great influence on the balance of supply and demand of electric energy and the tidal current characteristic of a power grid, and also influences the economic operation level and the grid loss characteristic of the power distribution network, so that the demand side response is utilized to improve and optimize the economic operation level of the power distribution network and reduce the grid loss of the power distribution network, which becomes a hot point problem concerned by people.
Disclosure of Invention
In order to overcome the defect that the response of a demand side is not considered in the conventional network loss optimization method, the invention provides a method which takes multi-time scale demand response modeling as a main body and then determines network loss from the aspect of economy so as to achieve the purpose of minimum network loss.
The technical scheme of the invention is as follows:
a network loss optimization method based on a multi-time scale demand response model comprises the following steps:
s1, before network loss optimization, a multi-time scale demand response model is constructed, a system is initialized, original data and data required for optimization are obtained, the data required for optimization comprise optimization variables, the optimization variables are the electricity prices of the front peak, the flat peak and the valley of the time-of-use electricity price and the long-term electricity price, and the process is as follows:
s11: analyzing medium and long term demand response characteristics to obtain annual average power demand;
the method takes the residential electricity demand as a research object, and selects a function expression of a medium-long term demand model as follows:
Inq＝0.601Iny-8Inp+0.877InS+2000 (1)
in the formula: y represents per-capita dominant income, yuan/year, p represents long-term electricity price, yuan/kilowatt-hour, S represents per-capita residential area, square meters per person, q represents per-year electricity demand, kilowatt-hour per year;
s12: analyzing short-term demand response characteristics to obtain 9 relevant short-term electricity quantity and price models;
the short-term electricity price model is established according to consumer psychology, time-of-use electricity price is used as a demand side response research object, a concept of load transfer rate is introduced, the meaning of the load transfer rate is that electricity consumption transferred from a time period with high electricity demand to a time period with low electricity demand is compared with the electricity consumption of the time period with high electricity demand, the short-term demand model of a user is fitted into a piecewise linear function through the existing electricity demand survey data, and the obtained function is as follows:
Figure GDA0002384377250000021
in the formula: lambda [ alpha ]ijAfter the electricity price representing the j time period is changed, the change of the demand of the i time period is caused; p is a radical ofjRepresents the electricity price of the j period; p represents a constant and refers to the price of electricity; k represents the slope of the user reactivity model; a represents a dead zone threshold; b represents a saturation region threshold;
the time-of-use electricity price means that each day is divided into a plurality of time periods of peak, flat and valley according to the load level of the system, and different time periods are executed in each time periodThe standard electricity price system of the electricity fee; dividing the time-of-use electricity price into three periods of peak, flat and valley, and expressing the three periods by f, p and g to obtain 9 related short-term electricity price models (p is as follows)j-p/p (j ═ f, p, g) is set in the range 0-1;
Figure GDA0002384377250000031
Figure GDA0002384377250000032
Figure GDA0002384377250000033
Figure GDA0002384377250000034
Figure GDA0002384377250000035
Figure GDA0002384377250000036
Figure GDA0002384377250000037
Figure GDA0002384377250000038
Figure GDA0002384377250000039
s13: combining the medium-long term demand model and the short term demand model by using the improved demand elastic matrix E to obtain a multi-time scale demand response model;
the multi-time scale demand response model is established based on a quantitative model of demand elasticity, peak, flat and valley time-sharing pricing is considered, and the short-term demand model and the medium-and-long-term demand model are combined through a demand elasticity matrix to form the multi-time scale demand response model;
the demand elasticity model is composed of an electricity quantity and electricity price elasticity matrix, the price elasticity of the electricity demand is regarded as the ratio of the change rate of the demand quantity to the change rate of the price, and the quantitative model of the demand elasticity is expressed by a demand elasticity coefficient:
Figure GDA0002384377250000041
in the formula, epsilonijI.e. the required elastic coefficient, Δ qi/qiIndicating a rate of change in demand for the i period; Δ pj/pjRepresents the rate of change of electricity prices for the j period; when i is j, epsilonijRepresenting the self-elastic coefficient, namely the change of the demand quantity before and after the time-of-use electricity price in the same time period, and the load of the change is called reducible load; when i ≠ j, εijRepresenting the mutual elasticity coefficient, namely the ratio of the change of the electricity price in the period j to the change of the demand quantity in the period i, and the part of the load is called transferable load;
dividing a day into n time intervals according to the required elasticity coefficient to obtain an n × n-order electric quantity price elasticity matrix as follows:
Figure GDA0002384377250000042
in the formula, diagonal elements are self-elastic coefficients, and the other elements are mutual elastic coefficients;
the multi-time scale demand response model is derived according to the formula (12), and the function expression of the model is as follows:
Figure GDA0002384377250000051
in the formula, qDR＝[qf-DRqp-DRqg-DR]T，qf-DR、qp-DR、qg-DRResponding to the electricity consumption of three periods of the back peak, the flat and the valley, qf、qp、qgImplementing the electricity consumption in three periods of front peak, flat and valley for the peak-valley time-of-use electricity price;
the improved requirement elasticity matrix E is formed by expressing the formulas (3) to (11) lambdaij(i ═ f, p, g; j ═ f, p, g) in formula (12) instead of Δ qi/qiPeak, flat, valley electric price pf、pp、pgAnd long-term price p, 9 ε are obtained by combining formulas (3) to (11)ij(i ═ f, p, g; j ═ f, p, g), and these 9 values were substituted into formula (13) to give:
Figure GDA0002384377250000052
wherein, the value on the diagonal represents the self-elasticity coefficient of the electricity price in three periods of peak, flat and valley, and the other 6 values represent the mutual elasticity coefficient of the electricity price in the periods of peak, flat and valley;
s2, establishing a model of the network loss optimization method, wherein the process is as follows:
s21: the objective function of the network loss optimization method is that the network loss in different time periods is multiplied by the electricity price in each time period respectively to obtain the lost electricity charge, the minimum value is taken as the optimization objective, and the expression of the objective function is as follows:
Figure GDA0002384377250000061
in the formula, pf、pp、pgRespectively representing the electricity prices of three periods of peak, flat and valley after the time-of-use electricity price is executed,
Figure GDA0002384377250000062
respectively representing the sum of the network loss of each node obtained by load flow calculation in three periods of peak, average and valley, wherein n represents the number of nodes in the power grid;
s22: the network loss optimization method has 4 optimization variables, which are as follows:
[pf、pp、pg、p]
wherein p isfDenotes the peak valence, ppMeans for indicating flatnessValence, pgRepresents a valley price, and p represents a long-term electricity price;
s23: the constraints of the electricity price pricing method are as follows:
Figure GDA0002384377250000063
Figure GDA0002384377250000064
Figure GDA0002384377250000065
qf-DR×pf+qp-DR×pp+qg-DR×pg≤q×p (20)
Uimin≤Ui≤Uimax(i＝1,2,…,n) (21)
Figure GDA0002384377250000071
|δi-δj|＜|δi-δj|max(23)
wherein:
the formula (17) is used for preventing inversion of peak, flat and valley electricity prices, and simultaneously preventing the electricity price in the peak period from being lower than the long-term electricity price, the electricity price in the valley period from being higher than the long-term electricity price, and the electricity price in the usual period has no special requirement;
the formula (18) sets the initial input of the price to a certain range, and prevents the electricity price from deviating far from the normal value;
equation (19) to prevent inversion of demand between the three periods of peak, flat and valley;
the formula (20) ensures that the user can benefit by paying less electricity than before the time-of-use electricity price implementation after the time-of-use electricity price implementation;
equations (21), (22), and (23) are constraint conditions for ensuring normal operation of the power system;
equation (21) is a constraint on all node voltages;
equation (22) is the active and reactive power constraints for all power nodes, where the active and reactive power of the PQ node have been given according to the conditions, and the active and reactive power of the PV node, the balance node, both need to satisfy the above conditions;
equation (23) is a phase requirement that some voltages between nodes need to meet, and some voltage phase differences between two ends of a line must be within a set range, so that stable operation of the system can be ensured.
And S3, solving the network loss optimization method model through a solving algorithm to obtain a network loss scheme.
Further, in step S3, a particle swarm algorithm is used as a solving algorithm, the power consumption obtained after the demand side response is optimized by the particle swarm algorithm, the obtained power consumption is used as an input of power flow calculation, and then the power flow calculation is performed by MATpower, so as to calculate the total net injection power of each node to represent the network loss.
According to the method, consumer psychology is considered in modeling, a long-term demand model and a short-term demand model are combined together through a demand elastic matrix, and a network loss optimization method based on a multi-time-scale demand side response model is established. The network loss optimization method based on the multi-time scale demand side response model provides a new idea for network loss optimization, and provides a reference for making electricity prices.
The invention takes the reduction of the network loss as the target, the network loss in different time periods is respectively multiplied by the electricity price in each time period, the lost electricity charge can be obtained, and the optimization target is the minimum value of the value. The network loss is obtained by carrying out load flow calculation through MATpower.
Drawings
FIG. 1 is an overall structure diagram of a multi-time scale demand response model construction of the invention.
Fig. 2 is a flow chart of a network loss optimization solution.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 and 2, a network loss optimization method based on a multi-time scale demand response model includes the following steps:
s1, before network loss optimization, a multi-time scale demand response model is constructed, a system is initialized, original data and data required for optimization are obtained, the data required for optimization comprise optimization variables, the optimization variables are the electricity prices of the front peak, the flat peak and the valley of the time-of-use electricity price and the long-term electricity price, and the process is as follows:
s11: analyzing medium and long term demand response characteristics to obtain annual average power demand;
the medium-long term demand model is divided into a plurality of models according to different research targets, the residential electricity demand is taken as a research object, and the function expression of the medium-long term demand model is selected as follows:
Inq＝0.601Iny-8Inp+0.877InS+2000 (1)
in the formula: y represents per-capita dominant income, yuan/year, p represents long-term electricity price, yuan/kilowatt-hour, S represents per-capita residential area, square meters per person, q represents per-year electricity demand, kilowatt-hour per year;
s12: analyzing short-term demand response characteristics to obtain 9 relevant short-term electricity quantity and price models;
the short-term electricity price model is mainly established according to consumer psychology, time-of-use electricity price is taken as a demand side response research object, a concept of load transfer rate is introduced, the meaning of the load transfer rate is the ratio of the electricity consumption transferred from a time period with high electricity demand to a time period with low electricity demand to the electricity consumption in a time period with high electricity demand, the short-term demand model of a user is fitted into a piecewise linear function through the existing electricity demand survey data, and the obtained function is as follows:
Figure GDA0002384377250000091
in the formula: lambda [ alpha ]ijAfter the electricity price representing the j time period is changed, the change of the demand of the i time period is caused; p is a radical ofjRepresents the electricity price of the j period; p represents a constant and refers to the price of electricity; k represents the slope of the user reactivity model; a represents a dead zone threshold; b represents a saturation region threshold;
the time-of-use price is the system of basisThe system load level is divided into a plurality of time intervals of peak, flat and valley every day, and the electricity price system with different electricity fee standards is executed in each time interval; in order to simplify the model, the time-of-use electricity price is divided into three periods of peak, flat and valley, and is expressed by f, p and g, and 9 related short-term electricity price models are obtained as follows, (p)jThe range of-p/p (j ═ f, p, g) is set to 0-1, where p is temporarily 0.5;
Figure GDA0002384377250000101
Figure GDA0002384377250000102
Figure GDA0002384377250000103
Figure GDA0002384377250000104
Figure GDA0002384377250000105
Figure GDA0002384377250000106
Figure GDA0002384377250000107
Figure GDA0002384377250000108
Figure GDA0002384377250000111
s13: combining the medium-long term demand model and the short term demand model by using the improved demand elastic matrix E to obtain a multi-time scale demand response model;
the overall structure of the multi-time scale demand response model is shown in FIG. 1.
The multi-time scale demand response model is established based on a quantitative model of demand elasticity, and peak, flat and valley time-sharing pricing is considered. The model combines a short-term demand model and a medium-term demand model through a demand elastic matrix to form a multi-time scale demand response model;
the demand elasticity model is composed of an electricity quantity and electricity price elasticity matrix, the price elasticity of the electricity demand can be considered as the ratio of the change rate of the demand quantity to the change rate of the price, and the quantitative model of the demand elasticity is expressed by a demand elasticity coefficient:
Figure GDA0002384377250000112
in the formula, epsilonijI.e. the required elastic coefficient, Δ qi/qiIndicating a rate of change in demand for the i period; Δ pj/pjIndicating the rate of change of electricity prices for period j. When i is j, epsilonijThe load representing the self-elastic coefficient, namely the change of the demand quantity before and after the time-of-use electricity price in the same time period, can also be called as reducible load, and people can avoid the part of load along with the increase of the electricity price, thereby achieving the purpose of reducing the electricity expense. When i ≠ j, εijRepresenting the mutual elasticity coefficient, namely the ratio of the electricity price change in the period j to the demand change in the period i, wherein the part of the load can also be called as a transferable load, and people can reduce the electricity consumption in the period i and transfer the part of the electricity to the period j along with the reduction of the electricity price in the period j;
dividing a day into n time intervals according to the required elasticity coefficient to obtain an n × n-order electric quantity price elasticity matrix as follows:
Figure GDA0002384377250000121
in the formula, diagonal elements are self-elastic coefficients, and the other elements are mutual elastic coefficients;
the multi-time scale demand response model is derived according to the formula (12), and the function expression of the model is as follows:
Figure GDA0002384377250000122
in the formula, qDR＝[qf-DRqp-DRqg-DR]T，qf-DR、qp-DR、qg-DRResponding to the electricity consumption of three periods of the back peak, the flat and the valley, qf、qp、qgImplementing the electricity consumption in three periods of front peak, flat and valley for the peak-valley time-of-use electricity price;
the improved requirement elasticity matrix E is formed by expressing the formulas (3) to (11) lambdaij(i ═ f, p, g; j ═ f, p, g) in formula (12) instead of Δ qi/qiPeak, flat, valley electric price pf、pp、pgAnd long-term price p, 9 ε are obtained by combining formulas (3) to (11)ij(i ═ f, p, g; j ═ f, p, g), and these 9 values were substituted into formula (13) to give:
Figure GDA0002384377250000123
wherein, the value on the diagonal represents the self-elasticity coefficient of the electricity price in three periods of peak, flat and valley, and the other 6 values represent the mutual elasticity coefficient of the electricity price in the periods of peak, flat and valley;
s2, establishing a model of the network loss optimization method, wherein the process is as follows:
the electric energy is inevitably lost due to the impedance of the line in the transmission process, and the loss does not bring any benefit but brings huge economic loss; the demand side response can change the network loss result by changing the active power input by each load in the power network, so as to achieve the aim of less economic loss; according to the invention, the power consumption of the user is changed after the demand side responds, which is equivalent to changing the power distribution in the network, and the purpose of reducing economic loss is finally achieved;
s21: the objective function of the network loss optimization method is that the network loss in different time periods is multiplied by the electricity price in each time period respectively to obtain the lost electricity charge, and the minimum value of the value is the optimization objective. The objective function is expressed as follows:
Figure GDA0002384377250000131
in the formula, pf、pp、pgRespectively representing the electricity prices of three periods of peak, flat and valley after the time-of-use electricity price is executed,
Figure GDA0002384377250000132
respectively representing the sum of the network loss of each node obtained by load flow calculation in three periods of peak, average and valley, wherein n represents the number of nodes in the power grid;
s22: the network loss optimization method has 4 optimization variables, which are as follows:
[pf、pp、pg、p]
wherein p isfDenotes the peak valence, ppDenotes the mean valence, pgRepresents a valley price, and p represents a long-term electricity price;
s23: the constraints of the electricity price pricing method are as follows:
Figure GDA0002384377250000141
Figure GDA0002384377250000142
Figure GDA0002384377250000143
qf-DR×pf+qp-DR×pp+qg-DR×pg≤q×p (20)
Uimin≤Ui≤Uimax(i＝1,2,…,n) (21)
Figure GDA0002384377250000144
|δi-δj|＜|δi-δj|max(23)
wherein:
the formula (17) is used for preventing inversion of peak, flat and valley electricity prices, and simultaneously preventing the electricity price in the peak period from being lower than the long-term electricity price, the electricity price in the valley period from being higher than the long-term electricity price, and the electricity price in the usual period has no special requirement;
the formula (18) sets the initial input of the price to a certain range, and prevents the electricity price from deviating far from the normal value;
equation (19) to prevent inversion of demand between the three periods of peak, flat and valley;
the formula (20) ensures that the user can benefit by paying less electricity than before the time-of-use electricity price implementation after the time-of-use electricity price implementation;
equations (21), (22), and (23) are constraint conditions for ensuring normal operation of the power system;
equation (21) is a constraint on all node voltages;
equation (22) is the active and reactive power constraints for all power nodes, where the active and reactive power of the PQ node have been given according to the conditions, and the active and reactive power of the PV node, the balance node, both need to satisfy the above conditions;
equation (23) is a phase requirement that some voltages between nodes need to meet, and some voltage phase differences between two ends of a line must be within a set range, so that stable operation of the system can be ensured.
And S3, solving the network loss optimization method model.
The invention considers that the high-speed calculation can be realized and the high-precision optimal solution can be obtained at the same time, and selects the particle swarm algorithm as the solving algorithm. Optimizing the power consumption obtained after the response of the demand side through a particle swarm algorithm, taking the obtained power consumption as the input of load flow calculation, performing the load flow calculation by using MATpower, and solving the total net injection power of each node to express the network loss.
The network loss optimization solving process is shown in fig. 2, and the specific steps of the network loss optimization method solving algorithm are as follows:
step 1: initializing variables, the speed and position of the particles, the time-of-use electricity price front peaks, flat and valley electricity prices and long-term electricity prices;
step 2: obtaining the current optimal optimization variable by utilizing a particle swarm algorithm, wherein the optimization variable comprises the following steps: peak, peace and valley electricity prices and long-term electricity prices;
and 3, step 3: obtaining the power consumption of the three periods of peak, flat and valley after the response of the demand side through the multi-time scale demand side response model established in the S13;
and 4, step 4: taking the power consumption obtained in the step 3 as the input of load flow calculation, performing load flow calculation by using MATpower to obtain the load branch network loss, and solving the total net injection power of each node to represent the network loss;
and 5, step 5: calculating a fitness function, and updating the position and the size of the new particle and an optimization variable according to the fitness function: peak, flat, valley and long-term electricity prices;
and 6, step 6: judging whether the maximum iteration times is reached;
and 7, step 7: if the maximum iteration times are reached, acquiring the optimal time-of-use electricity price and the target function reaching the minimum value; if not, repeating the steps 2 to 5 until the stop condition is met.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but includes equivalent technical means as would be recognized by those skilled in the art based on the inventive concept.

Claims (2)

1. A network loss optimization method based on a multi-time scale demand response model is characterized by comprising the following steps:
s1, before network loss optimization, a multi-time scale demand response model is constructed, a system is initialized, original data and data required for optimization are obtained, the data required for optimization comprise optimization variables, the optimization variables are the electricity prices of the front peak, the flat peak and the valley of the time-of-use electricity price and the long-term electricity price, and the process is as follows:
s11: analyzing medium and long term demand response characteristics to obtain annual average power demand;
the method takes the residential electricity demand as a research object, and selects a function expression of a medium-long term demand model as follows:
Inq＝0.601Iny-8Inp+0.877InS+2000 (1)
in the formula: y represents per-capita dominant income, yuan/year, p represents long-term electricity price, yuan/kilowatt-hour, S represents per-capita residential area, square meters per person, q represents per-year electricity demand, kilowatt-hour per year;
s12: analyzing short-term demand response characteristics to obtain 9 relevant short-term electricity quantity and price models;
the short-term electricity price model is established according to consumer psychology, time-of-use electricity price is used as a demand side response research object, a concept of load transfer rate is introduced, the meaning of the load transfer rate is that electricity consumption transferred from a time period with high electricity demand to a time period with low electricity demand is compared with the electricity consumption of the time period with high electricity demand, the short-term demand model of a user is fitted into a piecewise linear function through the existing electricity demand survey data, and the obtained function is as follows:
Figure FDA0002384377240000011
in the formula: lambda [ alpha ]ijAfter the electricity price representing the j time period is changed, the change of the demand of the i time period is caused; p is a radical ofjRepresents the electricity price of the j period; p represents a constant and refers to the price of electricity; k represents the slope of the user reactivity model; a represents a dead zone threshold; b represents a saturation region threshold;
the time-of-use electricity price is that each day is divided into a plurality of time intervals of peak, flat and valley according to the system load level, and each time interval executes an electricity price system with different electricity charge standards; dividing the time-of-use electricity price into three periods of peak, flat and valley, and expressing the three periods by f, p and g to obtain 9 related short-term electricity price models (p is as follows)j-p/p (j ═ f, p, g) is set in the range 0-1;
Figure FDA0002384377240000021
Figure FDA0002384377240000022
Figure FDA0002384377240000023
Figure FDA0002384377240000024
Figure FDA0002384377240000025
Figure FDA0002384377240000026
Figure FDA0002384377240000031
Figure FDA0002384377240000032
Figure FDA0002384377240000033
s13: combining the medium-long term demand model and the short term demand model by using the improved demand elastic matrix E to obtain a multi-time scale demand response model;
the multi-time scale demand response model is established based on a quantitative model of demand elasticity, peak, flat and valley time-sharing pricing is considered, and the short-term demand model and the medium-and-long-term demand model are combined through a demand elasticity matrix to form the multi-time scale demand response model;
the demand elasticity model is composed of an electricity quantity and electricity price elasticity matrix, the price elasticity of the electricity demand is regarded as the ratio of the change rate of the demand quantity to the change rate of the price, and the quantitative model of the demand elasticity is expressed by a demand elasticity coefficient:
Figure FDA0002384377240000034
in the formula, epsilonijI.e. the required elastic coefficient, Δ qi/qiIndicating a rate of change in demand for the i period; Δ pj/pjRepresents the rate of change of electricity prices for the j period; when i is j, epsilonijRepresenting the self-elastic coefficient, namely the change of the demand quantity before and after the time-of-use electricity price in the same time period, and the load of the change is called reducible load; when i ≠ j, εijRepresenting the mutual elasticity coefficient, namely the ratio of the change of the electricity price in the period j to the change of the demand quantity in the period i, and the part of the load is called transferable load;
dividing a day into n time intervals according to the required elasticity coefficient to obtain an n × n-order electric quantity price elasticity matrix as follows:
Figure FDA0002384377240000041
in the formula, diagonal elements are self-elastic coefficients, and the other elements are mutual elastic coefficients;
the multi-time scale demand response model is derived according to the formula (12), and the function expression of the model is as follows:
Figure FDA0002384377240000042
in the formula, qDR＝[qf-DRqp-DRqg-DR]T，qf-DR、qp-DR、qg-DRResponding to the electricity consumption of three periods of the back peak, the flat and the valley, qf、qp、qgImplementing the electricity consumption in three periods of front peak, flat and valley for the peak-valley time-of-use electricity price;
improved needThe elastic matrix E is represented by the following formulae (3) to (11) ×ij(i ═ f, p, g; j ═ f, p, g) in formula (12) instead of Δ qi/qiPeak, flat, valley electric price pf、pp、pgAnd long-term price p, 9 ε are obtained by combining formulas (3) to (11)ij(i ═ f, p, g; j ═ f, p, g), and these 9 values were substituted into formula (13) to give:
Figure FDA0002384377240000043
wherein, the value on the diagonal represents the self-elasticity coefficient of the electricity price in three periods of peak, flat and valley, and the other 6 values represent the mutual elasticity coefficient of the electricity price in the periods of peak, flat and valley;
s2, establishing a model of the network loss optimization method, wherein the process is as follows:
s21: the objective function of the network loss optimization method is that the network loss in different time periods is multiplied by the electricity price in each time period respectively to obtain the lost electricity charge, the minimum value is taken as the optimization objective, and the expression of the objective function is as follows:
Figure FDA0002384377240000051
in the formula, pf、pp、pgRespectively representing the electricity prices of three periods of peak, flat and valley after the time-of-use electricity price is executed,
Figure FDA0002384377240000052
respectively representing the sum of the network loss of each node obtained by load flow calculation in three periods of peak, average and valley, wherein n represents the number of nodes in the power grid;
s22: the network loss optimization method has 4 optimization variables, which are as follows:
[pf、pp、pg、p]
wherein p isfDenotes the peak valence, ppDenotes the mean valence, pgRepresents a valley price, and p represents a long-term electricity price;
s23: the constraints of the electricity price pricing method are as follows:
Figure FDA0002384377240000053
Figure FDA0002384377240000061
Figure FDA0002384377240000062
qf-DR×pf+qp-DR×pp+qg-DR×pg≤q×p (20)
Uimin≤Ui≤Uimax(i＝1,2,…,n) (21)
Figure FDA0002384377240000063
|δi-δj|＜|δi-δj|max(23)
wherein:
the formula (17) is used for preventing inversion of peak, flat and valley electricity prices, and simultaneously preventing the electricity price in the peak period from being lower than the long-term electricity price, the electricity price in the valley period from being higher than the long-term electricity price, and the electricity price in the usual period has no special requirement;
the formula (18) sets the initial input of the price to a certain range, and prevents the electricity price from deviating far from the normal value;
equation (19) to prevent inversion of demand between the three periods of peak, flat and valley;
the formula (20) ensures that the user can benefit by paying less electricity than before the time-of-use electricity price implementation after the time-of-use electricity price implementation;
equations (21), (22), and (23) are constraint conditions for ensuring normal operation of the power system;
equation (21) is a constraint on all node voltages;
equation (22) is the active and reactive power constraints for all power nodes, where the active and reactive power of the PQ node have been given according to the conditions, and the active and reactive power of the PV node, the balance node, both need to satisfy the above conditions;
the formula (23) is a phase requirement that the voltage between some nodes needs to meet, and the voltage phase difference between two ends of some lines must be within a set range, so that the stable operation of the system can be ensured;
and S3, solving the network loss optimization method model through a solving algorithm to obtain a network loss scheme.
2. The method as claimed in claim 1, wherein in step S3, a particle swarm algorithm is used as a solving algorithm, the power consumption obtained after the demand side response is optimized through the particle swarm algorithm, the obtained power consumption is used as an input of power flow calculation, and then MATpower is used to perform the power flow calculation, so as to obtain the total net injection power of each node to represent the network loss.
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