CN109687430A - Power distribution network economical operation method based on network reconfiguration and uncertain demand response - Google Patents
Power distribution network economical operation method based on network reconfiguration and uncertain demand response Download PDFInfo
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Abstract
A kind of power distribution network economical operation method based on network reconfiguration and uncertain demand response, comprising: establish the electric boiler model and heat-storing device model of user terminal respectively；Establish user demand response ambiguous model, the cost model responded including real response capacity model and stimulable type；Establish power distribution network economic load dispatching model, including objective function and constraint condition；Power distribution network economic load dispatching model is solved based on particle swarm algorithm.The present invention makes full use of system existing resource, it is possible to prevente effectively from complicated electrical network capacity construction and feeder line construction, reduce investment cost.
Description
Technical Field
The invention relates to an economic operation method of a power distribution network. In particular to a power distribution network economic operation method based on network reconstruction and uncertainty demand response, which is suitable for power distribution network optimization operation.
Background
The power distribution network is used as the final link for connecting electric energy from production to users, and safe and economic operation of the power distribution network is extremely important. The reconstruction of the power distribution network only needs to change the state of a contact switch or a section switch in the network, and can achieve the purposes of reducing network loss and improving reliability, economy and power supply benefits without increasing other investment, so that the reconstruction of the power distribution network is an important means for optimizing operation of the power distribution network, but along with the continuous increase of user-side multi-type loads in the urban power distribution network in the future, the peak load is increased obviously, the operation safety of the power distribution network is seriously influenced, and the simple reconstruction of the power distribution network cannot meet the operation requirement. Therefore, in order to solve the problem of operation safety brought by continuous increase of multiple energy sources at the user side of the power distribution network in the future, a certain demand response strategy needs to be introduced to ensure that the power distribution network can operate safely and economically after the load is increased. The intelligent power grid requires that the user enthusiasm is mobilized so as to achieve the purposes of peak clipping, valley filling and energy utilization rate improvement. Demand response is an important means of interaction between a power distribution network and users, and has two modes, namely price type load response and incentive type load response. The price type load response means that the power grid makes peak-valley electricity prices, and a user adjusts own electricity consumption according to the electricity prices, so that the uncertainty of the price type load response mainly comes from the uncertainty of a price demand curve; the incentive type load response refers to a response mode that a user contracts with an electric power company and accepts a signal of a dispatching department to reduce the load, and the user compensates for the load reduced by the electric power company to the user.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution network economic operation method based on network reconstruction and uncertain demand response, which can effectively avoid complex power grid capacity construction and feeder line construction.
The technical scheme adopted by the invention is as follows: a power distribution network economic operation method based on network reconstruction and uncertainty demand response comprises the following steps:
1) respectively establishing an electric boiler model and a heat storage device model of a user side;
2) establishing a user demand response uncertainty model comprising an actual response capacity model and an incentive response cost model;
3) establishing an economic dispatching model of the power distribution network, wherein the economic dispatching model comprises a target function and a constraint condition;
4) and solving the economic dispatching model of the power distribution network based on the particle swarm algorithm.
The electric boiler model in the step 1) refers to the relationship between electric power consumption and heat power generation of the electric boiler as follows:
Qb＝Pb·ηb(1)
in the formula, QbIndicating the heating power of the electric boiler ηbRepresenting the thermoelectric power ratio, PbRepresenting the electrical power required by the electric boiler to generate heat.
The heat storage device model in the step 1) refers to the relationship among heat storage capacity, input and output power and heat loss as follows:
S(t)＝S(t-1)+Phs(t)Δt-η×S(t-1) (2)
wherein S (t) and S (t-1) represent the energy stored in the heat storage device at time t and time t-1, respectively, and PhsThe representation represents the output power of the heat storage device at time t, and η represents the efficiency of the heat storage system.
The actual response capacity model in step 2) is as follows:
in the formula,actual response capacity; Δ pI，kFor planning response capacity, the constraints are:rI1and rI3Response deviation coefficients of a k-th gear allowed by a contract are respectively, k is a response gear, when k is 1, the response gear belongs to a reference response gear, and when k is 1, the response gear belongs to a reference response gear>1, belonging to the elastic response gear.
The cost model of the excitation type response in the step 2) is as follows:
in the formula, CIDRCost for stimulus-type response, cI,kThe unit compensation standard of the k-th gear is that k is a response gear, when k is 1, the unit compensation standard belongs to a reference response gear, and when k is 1>1, belonging to an elastic response gear;for actual response capacity, NIIn response to the number of gears.
The objective function in the step 3) is as follows:
in the formula, M is the total number of the branch circuits of the power distribution network; t is the total time of economic operation scheduling; pmAnd QmThe active power and the reactive power flowing through the head end of the branch m; u shapemIs the voltage on branch m; rmIs the impedance on branch m; cIDRThe cost of the stimulus-type response.
The constraint conditions in the step 3) comprise:
(3.1) power flow constraint conditions of the power distribution network:
in the formula, omegaiIs a set of nodes adjacent to node i; vi、VjAnd thetaijThe voltage amplitude and the phase angle difference of the node i and the node j are respectively; gii、Bii、GijAnd BijRespectively are self conductance, self susceptance, mutual conductance and mutual susceptance in the node admittance matrix; piAnd QiActive power and reactive power for node i;
(3.2) safe operation constraints including current constraints and voltage constraints:
Il≤Il maxl＝1,......Li(7)
VLi≤Vi≤VUii＝1,.....N (8)
in the formula IlIs the current flowing through element l; i islmaxMaximum allowed current for element/; l isiThe number of elements l; vLiIs the lower voltage limit of node i; vUiIs the voltage upper limit of the node i, and N is the number of nodes;
(3.3) radial network operation constraints:
gp∈Gp(9)
in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
(3.4) switch action frequency constraint:
Nz≤Nzmaxz∈S (10)
in the formula, NzIs the number of times of switch z action; n is a radical ofzmaxIs the upper limit of the number of times of the switch z; s is a switch number;
(3.5) N-1 constraint:
dw≥0 (11)
in the formula dwThe distance from the working point to the safety boundary of the power distribution network;
(3.6) electric boiler constraint:
Pb min≤Pb(t)≤Pb max(12)
Qb min≤Qb(t)≤Qb max(13)
in the formula, Pb(t) the electric power required by the electric boiler for generating heat at the moment t; pb min、Pb maxUpper and lower limits of electric power required for the boiler to generate heat; qb(t) the heating power of the electric boiler at the moment t; qb min、Qb maxThe upper limit and the lower limit of the heating power of the electric boiler are set;
(3.7) heat storage device restraint:
Phs min≤Phs(t)≤Phs max(14)
Smin≤S(t)≤Smax(15)
in the formula, Phs(t) is the output power of the heat storage device at time t; phs min、Phs maxThe upper limit and the lower limit of the output power of the heat storage device at the moment t; s (t) is the energy stored by the heat storage device at the moment t; smin、SmaxUpper and lower limits of energy stored in the heat storage device;
(3.8) incentive demand response cost constraints:
CIDR≤Cmax(16)
in the formula, CIDRCost for an excitation-type response; cmaxThe upper limit of the incentive response cost.
The step 4) comprises the following steps:
(4.1) inputting network structure parameters of the power distribution network, load data of each node and information of electricity price;
(4.2) judging whether the power distribution network meets the safe operation, if so, entering the step (4.8), otherwise, entering the step (4.3);
(4.3) initializing a quantum particle swarm algorithm, wherein the quantum particle swarm algorithm comprises all parameters of the algorithm and an initial particle swarm;
and (4.4) calculating an objective function and determining an individual fitness value.
(4.5) updating the particle positions to obtain an individual optimal solution and a global optimal solution;
(4.6) judging whether the iteration times X are exceeded, if yes, entering the step (4.7), if not, adding one to the iteration times, and returning to the step (4.4);
(4.7) outputting an optimized electricity price, total running cost and a power distribution network reconstruction result;
and (4.8) finishing.
Updating the particle positions in the step (4.5) to obtain an individual optimal solution and a global optimal solution, wherein the individual optimal solution and the global optimal solution are as follows:
θh＝(-1+2×rand0)×π/2 (17)
chrom＝[θh1,θh2,...,θhn](18)
dangle＝[Δθh1,Δθh2,...,Δθhn](19)
in the formula, thetahIs the phase angle of the h particle; thetahnIs the phase angle between the h particle and the n particle; delta thetahnIs the rotation angle between the h particle and the n particle; rand0Is [0,1 ]]A random number in between; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, selfchrom, respectivelyhIs the optimal position of the particle h, bestchrom is the optimal position of the population;
dangle(x+1)＝ω×dangle(x+1)+c1×r1×(selfchromh-chrom(t))+c1×r1×(bestchrom-chrom(x))(20)
chrom(x+1)＝chrom(x)+dangle(x+1) (21)
in the formula, ω is an inertia factor; c. C1And c2Normal, known as cognitive and social factors; r is1And r2Is [0,1 ]]Random numbers uniformly distributed among them;
for chromosome chrom (x) of the population in the x-th iteration, the phase angle of the g-th particle of chromosome chrom (x +1) of the x +1 generation is:
θhg(x+1)＝θhg(x)+sign(θbh(x)-θhg(x))Δθhg(x) (22)
in the formula,. DELTA.theta.hg(x) Is the rotation angle between h and g particles in the x-th iteration; thetabg(x) Is the phase angle of the h-th qubit of the chromosome corresponding to the optimal solution in the x-th iteration.
The power distribution network economic operation method based on network reconstruction and uncertain demand response combines two means of electric heating combined demand response and power distribution network reconstruction, and ensures safe and economic operation of the power distribution network. Through electric heating comprehensive demand response, demand side resources are enriched, the power distribution network is more flexibly scheduled, and peak clipping and valley filling are more effectively carried out; introducing incentive type demand response, reasonably formulating a compensation standard, and mobilizing the enthusiasm of users participating in demand response; the network structure can be adjusted through power distribution network reconstruction considering N-1 safety constraint, network supply load distribution is further balanced, and feasibility of a reconstruction result is guaranteed; the combination of the two can realize economic operation on the basis of guaranteeing the safe operation of the power distribution network. The method makes full use of the existing resources of the system, can effectively avoid complex power grid capacity construction and feeder line construction, and reduces investment cost.
Drawings
FIG. 1 is a schematic diagram of an actuation mechanism combining rigid constraint with elastic constraint;
fig. 2 is a schematic view of a quantum rotary gate.
Detailed Description
The method for economically operating a power distribution network based on network reconfiguration and uncertain demand response according to the present invention is described in detail with reference to the following embodiments and accompanying drawings.
The invention discloses a power distribution network economic operation method based on network reconstruction and uncertainty demand response, which comprises the following steps:
1) respectively establishing an electric boiler model and a heat storage device model of a user side; wherein,
the electric boiler is a device for realizing load side electric heating coupling, can realize electric heating conversion, converts electric energy into heat energy, supplies heat load and stores more heat energy in the heat storage device. The electric boiler model refers to the relationship between electric power consumption and heat power generation of the electric boiler as follows:
Qb＝Pb·ηb(1)
in the formula, QbIndicating the heating power of the electric boiler ηbRepresenting the thermoelectric power ratio, PbRepresenting the electrical power required by the electric boiler to generate heat.
The heat storage device is generally a heat storage tank, a heat storage tank and the like, and the heat storage device model refers to the relationship among heat storage capacity, input and output power and heat loss as follows:
S(t)＝S(t-1)+Phs(t)Δt-η×S(t-1) (2)
wherein S (t) and S (t-1) represent the energy stored in the heat storage device at time t and time t-1, respectively, and PhsThe representation represents the output power of the heat storage device at time t, and η represents the efficiency of the heat storage system.
2) Establishing a model for uncertain user demand response
The electric load response is mainly realized by the control of an electric boiler and a heat storage device through interruptable load and translatable load to reduce peak clipping and valley filling of the electric load. The uncertainty of the thermal load response is derived from the fact that the user's ambient temperature requirement is not a specific value, but rather a comfort interval, and thus the uncertainty of the ambient temperature within this interval causes the uncertainty of the demand response, and from the uncertainty of the equivalent electrical load response after converting the thermal load demand into the electrical load demand. Uncertainty of incentive-type load response is mainly derived from uncertainty of response of user incentive policy. The actual response capacity of the incentive type demand response may deviate from the expectation, and the uncertainty of the response is mainly derived from factors such as the estimation of the baseline load, the response execution of the user, and the uncertainty of the demand for load shedding.
In the invention, an incentive mechanism combining rigid constraint and elastic constraint is adopted, and incentive type demand response of a user is divided into a reference response file and an elastic response file by contract agreement, wherein the reference response is set as the rigid constraint, and the actual response capacity of the rigid constraint is equal to the planned response capacity; the elastic response profile is set to an elastic constraint that allows its actual response capacity to fluctuate within a certain range of the projected response capacity. The reference response file corresponds to the reference subsidy, and the response increment on the basis belongs to the elastic response file and respectively corresponds to different subsidy standards.
After considering the influence of response uncertainty, the contract between the power grid and the user shall agree on ① the response capacity upper limit and subsidy standard of the reference gear of the user, ② the response capacity upper limit, the actually-executed allowable deviation ratio and the subsidy standard of each flexible gear of the user.
The user will have different response capacities for different subsidy criteria, i.e. the user's thermal load demand will fluctuate within the comfort interval, and the user's electrical load demand will fluctuate as the interruptible and transferable load amounts change. Under different subsidy criteria, the user needs to meet the benchmark response, and the elastic response part depends on the subjective factors of the user. When k is 1, the response gear belongs to the benchmark response gear, and when k is 2, the response gear belongs to the elastic response gear. As shown in FIG. 1, cI,kA unit compensation criterion for the k-th gear is expressed,and Δ pI,kThe actual response capacity and the planned response capacity of the k-th gear are respectively.
The user demand response uncertainty model comprises an actual response capacity model and an incentive type response cost model; wherein,
the actual response capacity model is as follows:
in the formula,actual response capacity; Δ pI，kFor planning response capacity, the constraints are:rI1and rI3Response deviation coefficients of a k-th gear allowed by a contract are respectively, k is a response gear, when k is 1, the response gear belongs to a reference response gear, and when k is 1, the response gear belongs to a reference response gear>1, belonging to the elastic response gear.
The cost model of the excitation type response is as follows:
in the formula, CIDRCost for stimulus-type response, cI,kThe unit compensation standard of the k-th gear is that k is a response gear, when k is 1, the unit compensation standard belongs to a reference response gear, and when k is 1>1, belonging to an elastic response gear;for actual response capacity, NIIn response to the number of gears.
3) Establishing economic dispatching model of power distribution network
When the power consumption is high, the network loss cost of the power distribution network is increased, and certain potential safety hazards are brought. According to the invention, a certain demand response strategy is introduced, so that the network loss is reduced and the safe and economic operation of the power distribution network is ensured. At the same time, the introduction of demand response may require a certain cost. Therefore, the optimal scheduling model of the power distribution network is constructed by optimizing the on-off state of the switch of the power distribution network and the unit compensation standard of each response gear and aiming at the minimum total running cost of the power distribution network within 24 hours a day, namely the minimum sum of the network loss cost and the demand response cost, so that the economic scheduling of the power distribution network is realized.
The economic dispatching model of the power distribution network comprises a target function and a constraint condition; wherein
The objective function is as follows:
in the formula, M is the total number of the branch circuits of the power distribution network; t is the total time of economic operation scheduling; pmAnd QmThe active power and the reactive power flowing through the head end of the branch m; u shapemIs the voltage on branch m; rmIs the impedance on branch m; cIDRThe cost of the stimulus-type response.
The constraint conditions comprise:
(3.1) power flow constraint conditions of the power distribution network:
in the formula, omegaiIs a set of nodes adjacent to node i; vi、VjAnd thetaijThe voltage amplitude and the phase angle difference of the node i and the node j are respectively; gii、Bii、GijAnd BijRespectively are self conductance, self susceptance, mutual conductance and mutual susceptance in the node admittance matrix; piAnd QiActive power and reactive power for node i;
(3.2) safe operation constraints including current constraints and voltage constraints:
Il≤Il maxl＝1,......Li(7)
VLi≤Vi≤VUii＝1,.....N (8)
in the formula IlIs the current flowing through element l; i islmaxMaximum allowed current for element/; l isiThe number of elements l; vLiIs the lower voltage limit of node i; vUiIs the voltage upper limit of the node i, and N is the number of nodes;
(3.3) radial network operation constraints:
gp∈Gp(9)
in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
(3.4) switch action frequency constraint:
Nz≤Nzmaxz∈S (10)
in the formula, NzIs the number of times of switch z action; n is a radical ofzmaxIs the upper limit of the number of times of the switch z; s is a switch number;
(3.5) N-1 constraint:
safety distance d of distribution networkwThe distance between the working point and the safety boundary of the power distribution network reflects the position of the working point in the safety domain of the power distribution network, and when the working point does not meet the N-1 safety, the safety distance is a negative value; when the working point meets the N-1 safety, the safety distance is a positive value, and the larger the absolute value is, the higher the safety degree of the working point is.
dw≥0 (11)
In the formula dwThe distance from the working point to the safety boundary of the power distribution network;
(3.6) electric boiler constraint:
Pb min≤Pb(t)≤Pb max(12)
Qb min≤Qb(t)≤Qb max(13)
in the formula, Pb(t) the electric power required by the electric boiler for generating heat at the moment t; pb min、Pb maxUpper and lower limits of electric power required for the boiler to generate heat; qb(t) the heating power of the electric boiler at the moment t; qb min、Qb maxThe upper limit and the lower limit of the heating power of the electric boiler are set;
(3.7) heat storage device restraint:
Phs min≤Phs(t)≤Phs max(14)
Smin≤S(t)≤Smax(15)
in the formula, Phs(t) is the output power of the heat storage device at time t; phs min、Phs maxThe upper limit and the lower limit of the output power of the heat storage device at the moment t; s (t) is the energy stored by the heat storage device at the moment t; smin、SmaxUpper and lower limits of energy stored in the heat storage device;
(3.8) incentive demand response cost constraints:
CIDR≤Cmax(16)
in the formula, CIDRCost for an excitation-type response; cmaxThe upper limit of the incentive response cost.
4) And solving the economic dispatching model of the power distribution network based on the particle swarm algorithm. The method comprises the following steps:
the solving algorithm adopted by the invention is quantum particle swarm algorithm (QPSO), compared with the general particle swarm algorithm, the QPSO has the improvement that the movement and the variation of the particles are carried out in a two-dimensional quantum space, so that the particles can cover the region which can not be reached in the one-dimensional solution space range in single search or variation, and the convergence of the optimization problem is accelerated to a certain degree.
The QPSO algorithm maps the actual solution space to the quantum space according to a certain rule. The current position of the particle is represented by the probability amplitude of the qubit, and then the solution space transformation is carried out, and each probability amplitude of the qubit represents a variable of the solution space. The state updates of the particles include location updates and velocity updates. The updating of the particle moving speed in the prior art is replaced by the updating of the rotation angle of the quantum rotating gate, and the updating of the particle position is replaced by the updating of the quantum bit probability amplitude. A schematic diagram of a quantum rotary gate is shown in fig. 2.
(4.1) inputting network structure parameters of the power distribution network, load data of each node and information of electricity price;
(4.2) judging whether the power distribution network meets the safe operation, if so, entering the step (4.8), otherwise, entering the step (4.3);
(4.3) initializing a quantum particle swarm algorithm, wherein the quantum particle swarm algorithm comprises all parameters of the algorithm and an initial particle swarm;
and (4.4) calculating an objective function and determining an individual fitness value.
(4.5) updating the particle positions to obtain an individual optimal solution and a global optimal solution; the updating of the particle positions to obtain the individual optimal solution and the global optimal solution is as follows:
θh＝(-1+2×rand0)×π/2 (17)
chrom＝[θh1,θh2,...,θhn](18)
dangle＝[Δθh1,Δθh2,...,Δθhn](19)
in the formula, thetahIs the phase angle of the h particle; thetahnIs the phase angle between the h particle and the n particle; delta thetahnIs the rotation angle between the h particle and the n particle; rand0Is [0,1 ]]A random number in between; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, selfchrom, respectivelyhIs the optimal position of the particle h, bestchrom is the optimal position of the population;
dangle(x+1)＝ω×dangle(x+1)+c1×r1×(selfchromh-chrom(t))+c1×r1×(bestchrom-chrom(x)) (20)
chrom(x+1)＝chrom(x)+dangle(x+1) (21)
in the formula, ω is an inertia factor; c. C1And c2Normal, known as cognitive and social factors; r is1And r2Is [0,1 ]]Random numbers uniformly distributed among them;
for chromosome chrom (x) of the population in the x-th iteration, the phase angle of the g-th particle of chromosome chrom (x +1) of the x +1 generation is:
θhg(x+1)＝θhg(x)+sign(θbh(x)-θhg(x))Δθhg(x) (22)
in the formula,. DELTA.theta.hg(x) Is the rotation angle between h and g particles in the x-th iteration; thetabg(x) Is the phase angle of the h-th qubit of the chromosome corresponding to the optimal solution in the x-th iteration.
(4.6) judging whether the iteration times X are exceeded, if yes, entering the step (4.7), if not, adding one to the iteration times, and returning to the step (4.4);
(4.7) outputting an optimized electricity price, total running cost and a power distribution network reconstruction result;
and (4.8) finishing.
Claims (9)
1. A power distribution network economic operation method based on network reconstruction and uncertainty demand response is characterized by comprising the following steps:
1) respectively establishing an electric boiler model and a heat storage device model of a user side;
2) establishing a user demand response uncertainty model comprising an actual response capacity model and an incentive response cost model;
3) establishing an economic dispatching model of the power distribution network, wherein the economic dispatching model comprises a target function and a constraint condition;
4) and solving the economic dispatching model of the power distribution network based on the particle swarm algorithm.
2. The method for economic operation of a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein said electric boiler model of step 1) is the relationship between electric power consumed and thermal power generated by the electric boiler as follows:
Qb＝Pb·ηb(1)
in the formula, QbIndicating the heating power of the electric boiler ηbRepresenting the thermoelectric power ratio, PbRepresenting the electrical power required by the electric boiler to generate heat.
3. The method as claimed in claim 1, wherein the heat storage device model in step 1) is a relationship among heat storage capacity, input/output power and heat loss, and is as follows:
S(t)＝S(t-1)+Phs(t)Δt-η×S(t-1) (2)
wherein S (t) and S (t-1) represent the energy stored in the heat storage device at time t and time t-1, respectively, and PhsThe representation represents the output power of the heat storage device at time t, and η represents the efficiency of the heat storage system.
4. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the actual response capacity model of step 2) is as follows:
in the formula,actual response capacity; Δ pI,kFor planning response capacity, the constraints are:rI1and rI3Response deviation coefficients of a k-th gear allowed by a contract are respectively, k is a response gear, when k is 1, the response gear belongs to a reference response gear, and when k is 1, the response gear belongs to a reference response gear>1, belonging to the elastic response gear.
5. The method for economically operating a power distribution network based on network reconstruction and uncertainty demand response as claimed in claim 1, wherein the cost model of incentive type response in step 2) is:
in the formula, CIDRCost for stimulus-type response, cI,kThe unit compensation standard of the k-th gear is that k is a response gear, when k is 1, the unit compensation standard belongs to a reference response gear, and when k is 1>1, belonging to an elastic response gear;for actual response capacity, NIIn response to the number of gears.
6. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the objective function of step 3) is:
in the formula, M is the total number of the branch circuits of the power distribution network; t is the total time of economic operation scheduling; pmAnd QmThe active power and the reactive power flowing through the head end of the branch m; u shapemIs the voltage on branch m; rmIs the impedance on branch m; cIDRThe cost of the stimulus-type response.
7. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the constraint conditions in step 3) comprise:
(3.1) power flow constraint conditions of the power distribution network:
in the formula, omegaiIs a set of nodes adjacent to node i; vi、VjAnd thetaijThe voltage amplitude and the phase angle difference of the node i and the node j are respectively; gii、Bii、GijAnd BijRespectively are self conductance, self susceptance, mutual conductance and mutual susceptance in the node admittance matrix; piAnd QiActive power and reactive power for node i;
(3.2) safe operation constraints including current constraints and voltage constraints:
Il≤Ilmaxl＝1,......Li(7)
VLi≤Vi≤VUii＝1,.....N (8)
in the formula IlIs the current flowing through element l; i islmaxMaximum allowed current for element/; l isiThe number of elements l; vLiIs the lower voltage limit of node i; vUiIs the voltage upper limit of the node i, and N is the number of nodes;
(3.3) radial network operation constraints:
gp∈Gp(9)
in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
(3.4) switch action frequency constraint:
Nz≤Nzmaxz∈S(10)
in the formula, NzIs the number of times of switch z action; n is a radical ofzmaxIs the upper limit of the number of times of the switch z; s is a switch number;
(3.5) N-1 constraint:
dw≥0 (11)
in the formula dwThe distance from the working point to the safety boundary of the power distribution network;
(3.6) electric boiler constraint:
Pbmin≤Pb(t)≤Pbmax(12)
Qbmin≤Qb(t)≤Qbmax(13)
in the formula, Pb(t) the electric power required by the electric boiler for generating heat at the moment t; pbmin、PbmaxUpper and lower limits of electric power required for the boiler to generate heat; qb(t) the heating power of the electric boiler at the moment t; qbmin、QbmaxThe upper limit and the lower limit of the heating power of the electric boiler are set;
(3.7) heat storage device restraint:
Phsmin≤Phs(t)≤Phsmax(14)
Smin≤S(t)≤Smax(15)
in the formula, Phs(t) is the output power of the heat storage device at time t; phsmin、PhsmaxThe upper limit and the lower limit of the output power of the heat storage device at the moment t; s (t) is the energy stored by the heat storage device at the moment t; smin、SmaxUpper and lower limits of energy stored in the heat storage device;
(3.8) incentive demand response cost constraints:
CIDR≤Cmax(16)
in the formula, CIDRCost for an excitation-type response; cmaxThe upper limit of the incentive response cost.
8. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response as set forth in claim 1, wherein step 4) comprises:
(4.1) inputting network structure parameters of the power distribution network, load data of each node and information of electricity price;
(4.2) judging whether the power distribution network meets the safe operation, if so, entering the step (4.8), otherwise, entering the step (4.3);
(4.3) initializing a quantum particle swarm algorithm, wherein the quantum particle swarm algorithm comprises all parameters of the algorithm and an initial particle swarm;
and (4.4) calculating an objective function and determining an individual fitness value.
(4.5) updating the particle positions to obtain an individual optimal solution and a global optimal solution;
(4.6) judging whether the iteration times X are exceeded, if yes, entering the step (4.7), if not, adding one to the iteration times, and returning to the step (4.4);
(4.7) outputting an optimized electricity price, total running cost and a power distribution network reconstruction result;
and (4.8) finishing.
9. The method for economic operation of a power distribution network based on network reconstruction and uncertainty demand response of claim 8 wherein the updating of particle locations in step (4.5) results in individual optimal solutions and global optimal solutions that are:
θh＝(-1+2×rand0)×π/2 (17)
chrom＝[θh1,θh2,...,θhn](18)
dangle＝[Δθh1,Δθh2,...,Δθhn](19)
in the formula, thetahIs the phase angle of the h particle; thetahnIs the phase angle between the h particle and the n particle; delta thetahnIs the rotation angle between the h particle and the n particle; rand0Is [0,1 ]]A random number in between; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, selfchrom, respectivelyhIs the optimal position of the particle h, bestchrom is the optimal position of the population;
dangle(x+1)＝ω×dangle(x+1)+c1×r1×(selfchromh-chrom(t))+c1×r1×(bestchrom-chrom(x))
(20)
chrom(x+1)＝chrom(x)+dangle(x+1) (21)
in the formula, ω is an inertia factor; c. C1And c2Normal, known as cognitive and social factors; r is1And r2Is [0,1 ]]All areUniformly distributed random numbers;
for chromosome chrom (x) of the population in the x-th iteration, the phase angle of the g-th particle of chromosome chrom (x +1) of the x +1 generation is:
θhg(x+1)＝θhg(x)+sign(θbh(x)-θhg(x))Δθhg(x) (22)
in the formula,. DELTA.theta.hg(x) Is the rotation angle between h and g particles in the x-th iteration; thetabg(x) Is the phase angle of the h-th qubit of the chromosome corresponding to the optimal solution in the x-th iteration.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150213466A1 (en) * | 2014-01-24 | 2015-07-30 | Fujitsu Limited | Demand response aggregation optimization |
| CN107979092A (en) * | 2017-12-18 | 2018-05-01 | 国网宁夏电力有限公司经济技术研究院 | It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access |
| CN108462175A (en) * | 2018-05-10 | 2018-08-28 | 中国电力科学研究院有限公司 | A kind of electric heating equipment demand response interactive approach, system and device |
| CN108470233A (en) * | 2018-02-01 | 2018-08-31 | 华北电力大学 | A kind of the demand response capability assessment method and computing device of intelligent grid |
-
2018
- 2018-11-30 CN CN201811460256.5A patent/CN109687430B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150213466A1 (en) * | 2014-01-24 | 2015-07-30 | Fujitsu Limited | Demand response aggregation optimization |
| CN107979092A (en) * | 2017-12-18 | 2018-05-01 | 国网宁夏电力有限公司经济技术研究院 | It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access |
| CN108470233A (en) * | 2018-02-01 | 2018-08-31 | 华北电力大学 | A kind of the demand response capability assessment method and computing device of intelligent grid |
| CN108462175A (en) * | 2018-05-10 | 2018-08-28 | 中国电力科学研究院有限公司 | A kind of electric heating equipment demand response interactive approach, system and device |
Non-Patent Citations (1)
| Title |
|---|
| 孙宇军等: "考虑需求响应不确定性的多时间尺度源荷互动决策方法", 《电力系统自动化》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110460043A (en) * | 2019-08-08 | 2019-11-15 | 武汉理工大学 | The distribution network structure reconstructing method of particle swarm algorithm is improved based on multiple target |
| CN110460043B (en) * | 2019-08-08 | 2020-11-24 | 武汉理工大学 | Power distribution network frame reconstruction method based on multi-target improved particle swarm algorithm |
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